This R markdown document provides an example of the basic sequence of commands in R to:

In this case we will download US COVID-19 infection data for each US county and combine those data with demongraphic data from the US Census Bureau to gain an understanding of per-capita rates of increase in infection relative to some demographic variables of interest.

Import Libraries

knitr::opts_chunk$set(message = FALSE)
options("scipen"=100, "digits"=4) # tune up when numbers will be displayed in fixed vs. scientific notation
library(tidyverse)    # core meta-package for a bunch of the tidyverse packages
## ── Attaching packages ────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.3
## ✓ tibble  2.1.3     ✓ dplyr   0.8.4
## ✓ tidyr   1.0.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.4.0
## ── Conflicts ───────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(readxl)       # read XLS files into a datafram
library(lubridate)    # convenience functions for processing dates
## 
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
## 
##     date
library(ggthemes)     # add some great themes to use in our plots
library(RColorBrewer) # add the color brewer color palette
library(knitr)        # combined with kableExtra output tibbles as nicely formatted tables  
library(kableExtra)   # combined with kableExtra output tibbles as nicely formatted tables
## 
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
## 
##     group_rows

Import data

To perform our analysis we are going to retrieve and import three datasets:

ACS5 data import

These data will be imported from a previously downloaded copy of the data from the US Census Bureau web site (referenced above). These data come in the form of a CSV file and an associated CSV file that describes the content of each of the columns in the file. We will use the Tidyverse read_csv command to import this file from the local data folder.

# relative path and filename for the csv file to be imported
acs5_filepath <- "data/ACS2018/ACSST5Y2018.S0101_data_with_overlays_2020-04-06T234438.csv"
acs5_raw <- read_csv(acs5_filepath, col_names = TRUE, skip = 1)

Column specifications

glimpse(acs5_raw)
## Observations: 3,220
## Variables: 458
## $ id                                                                                                             <chr> …
## $ `Geographic Area Name`                                                                                         <chr> …
## $ `Estimate!!Total!!Total population`                                                                            <dbl> …
## $ `Margin of Error!!Total MOE!!Total population`                                                                 <chr> …
## $ `Estimate!!Percent!!Total population`                                                                          <chr> …
## $ `Margin of Error!!Percent MOE!!Total population`                                                               <chr> …
## $ `Estimate!!Male!!Total population`                                                                             <dbl> …
## $ `Margin of Error!!Male MOE!!Total population`                                                                  <chr> …
## $ `Estimate!!Percent Male!!Total population`                                                                     <chr> …
## $ `Margin of Error!!Percent Male MOE!!Total population`                                                          <chr> …
## $ `Estimate!!Female!!Total population`                                                                           <dbl> …
## $ `Margin of Error!!Female MOE!!Total population`                                                                <chr> …
## $ `Estimate!!Percent Female!!Total population`                                                                   <chr> …
## $ `Margin of Error!!Percent Female MOE!!Total population`                                                        <chr> …
## $ `Estimate!!Total!!Total population!!AGE!!Under 5 years`                                                        <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!Under 5 years`                                             <chr> …
## $ `Estimate!!Percent!!Total population!!AGE!!Under 5 years`                                                      <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!Under 5 years`                                           <chr> …
## $ `Estimate!!Male!!Total population!!AGE!!Under 5 years`                                                         <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!Under 5 years`                                              <chr> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!Under 5 years`                                                 <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!Under 5 years`                                      <chr> …
## $ `Estimate!!Female!!Total population!!AGE!!Under 5 years`                                                       <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!Under 5 years`                                            <chr> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!Under 5 years`                                               <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!Under 5 years`                                    <chr> …
## $ `Estimate!!Total!!Total population!!AGE!!5 to 9 years`                                                         <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!5 to 9 years`                                              <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!5 to 9 years`                                                       <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!5 to 9 years`                                            <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!5 to 9 years`                                                          <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!5 to 9 years`                                               <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!5 to 9 years`                                                  <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!5 to 9 years`                                       <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!5 to 9 years`                                                        <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!5 to 9 years`                                             <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!5 to 9 years`                                                <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!5 to 9 years`                                     <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!10 to 14 years`                                                       <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!10 to 14 years`                                            <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!10 to 14 years`                                                     <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!10 to 14 years`                                          <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!10 to 14 years`                                                        <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!10 to 14 years`                                             <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!10 to 14 years`                                                <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!10 to 14 years`                                     <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!10 to 14 years`                                                      <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!10 to 14 years`                                           <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!10 to 14 years`                                              <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!10 to 14 years`                                   <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!15 to 19 years`                                                       <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!15 to 19 years`                                            <chr> …
## $ `Estimate!!Percent!!Total population!!AGE!!15 to 19 years`                                                     <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!15 to 19 years`                                          <chr> …
## $ `Estimate!!Male!!Total population!!AGE!!15 to 19 years`                                                        <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!15 to 19 years`                                             <chr> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!15 to 19 years`                                                <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!15 to 19 years`                                     <chr> …
## $ `Estimate!!Female!!Total population!!AGE!!15 to 19 years`                                                      <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!15 to 19 years`                                           <chr> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!15 to 19 years`                                              <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!15 to 19 years`                                   <chr> …
## $ `Estimate!!Total!!Total population!!AGE!!20 to 24 years`                                                       <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!20 to 24 years`                                            <chr> …
## $ `Estimate!!Percent!!Total population!!AGE!!20 to 24 years`                                                     <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!20 to 24 years`                                          <chr> …
## $ `Estimate!!Male!!Total population!!AGE!!20 to 24 years`                                                        <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!20 to 24 years`                                             <chr> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!20 to 24 years`                                                <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!20 to 24 years`                                     <chr> …
## $ `Estimate!!Female!!Total population!!AGE!!20 to 24 years`                                                      <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!20 to 24 years`                                           <chr> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!20 to 24 years`                                              <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!20 to 24 years`                                   <chr> …
## $ `Estimate!!Total!!Total population!!AGE!!25 to 29 years`                                                       <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!25 to 29 years`                                            <chr> …
## $ `Estimate!!Percent!!Total population!!AGE!!25 to 29 years`                                                     <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!25 to 29 years`                                          <chr> …
## $ `Estimate!!Male!!Total population!!AGE!!25 to 29 years`                                                        <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!25 to 29 years`                                             <chr> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!25 to 29 years`                                                <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!25 to 29 years`                                     <chr> …
## $ `Estimate!!Female!!Total population!!AGE!!25 to 29 years`                                                      <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!25 to 29 years`                                           <chr> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!25 to 29 years`                                              <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!25 to 29 years`                                   <chr> …
## $ `Estimate!!Total!!Total population!!AGE!!30 to 34 years`                                                       <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!30 to 34 years`                                            <chr> …
## $ `Estimate!!Percent!!Total population!!AGE!!30 to 34 years`                                                     <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!30 to 34 years`                                          <chr> …
## $ `Estimate!!Male!!Total population!!AGE!!30 to 34 years`                                                        <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!30 to 34 years`                                             <chr> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!30 to 34 years`                                                <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!30 to 34 years`                                     <chr> …
## $ `Estimate!!Female!!Total population!!AGE!!30 to 34 years`                                                      <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!30 to 34 years`                                           <chr> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!30 to 34 years`                                              <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!30 to 34 years`                                   <chr> …
## $ `Estimate!!Total!!Total population!!AGE!!35 to 39 years`                                                       <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!35 to 39 years`                                            <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!35 to 39 years`                                                     <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!35 to 39 years`                                          <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!35 to 39 years`                                                        <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!35 to 39 years`                                             <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!35 to 39 years`                                                <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!35 to 39 years`                                     <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!35 to 39 years`                                                      <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!35 to 39 years`                                           <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!35 to 39 years`                                              <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!35 to 39 years`                                   <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!40 to 44 years`                                                       <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!40 to 44 years`                                            <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!40 to 44 years`                                                     <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!40 to 44 years`                                          <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!40 to 44 years`                                                        <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!40 to 44 years`                                             <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!40 to 44 years`                                                <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!40 to 44 years`                                     <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!40 to 44 years`                                                      <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!40 to 44 years`                                           <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!40 to 44 years`                                              <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!40 to 44 years`                                   <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!45 to 49 years`                                                       <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!45 to 49 years`                                            <chr> …
## $ `Estimate!!Percent!!Total population!!AGE!!45 to 49 years`                                                     <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!45 to 49 years`                                          <chr> …
## $ `Estimate!!Male!!Total population!!AGE!!45 to 49 years`                                                        <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!45 to 49 years`                                             <chr> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!45 to 49 years`                                                <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!45 to 49 years`                                     <chr> …
## $ `Estimate!!Female!!Total population!!AGE!!45 to 49 years`                                                      <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!45 to 49 years`                                           <chr> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!45 to 49 years`                                              <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!45 to 49 years`                                   <chr> …
## $ `Estimate!!Total!!Total population!!AGE!!50 to 54 years`                                                       <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!50 to 54 years`                                            <chr> …
## $ `Estimate!!Percent!!Total population!!AGE!!50 to 54 years`                                                     <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!50 to 54 years`                                          <chr> …
## $ `Estimate!!Male!!Total population!!AGE!!50 to 54 years`                                                        <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!50 to 54 years`                                             <chr> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!50 to 54 years`                                                <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!50 to 54 years`                                     <chr> …
## $ `Estimate!!Female!!Total population!!AGE!!50 to 54 years`                                                      <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!50 to 54 years`                                           <chr> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!50 to 54 years`                                              <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!50 to 54 years`                                   <chr> …
## $ `Estimate!!Total!!Total population!!AGE!!55 to 59 years`                                                       <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!55 to 59 years`                                            <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!55 to 59 years`                                                     <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!55 to 59 years`                                          <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!55 to 59 years`                                                        <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!55 to 59 years`                                             <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!55 to 59 years`                                                <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!55 to 59 years`                                     <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!55 to 59 years`                                                      <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!55 to 59 years`                                              <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!55 to 59 years`                                   <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!60 to 64 years`                                                       <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!60 to 64 years`                                            <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!60 to 64 years`                                                     <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!60 to 64 years`                                          <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!60 to 64 years`                                                        <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!60 to 64 years`                                             <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!60 to 64 years`                                                <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!60 to 64 years`                                     <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!60 to 64 years`                                                      <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!60 to 64 years`                                           <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!55 to 59 years`                                           <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!60 to 64 years`                                              <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!60 to 64 years`                                   <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!65 to 69 years`                                                       <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!65 to 69 years`                                            <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!65 to 69 years`                                                     <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!65 to 69 years`                                          <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!65 to 69 years`                                                        <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!65 to 69 years`                                             <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!65 to 69 years`                                                <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!65 to 69 years`                                     <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!65 to 69 years`                                                      <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!65 to 69 years`                                           <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!65 to 69 years`                                              <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!65 to 69 years`                                   <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!70 to 74 years`                                                       <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!70 to 74 years`                                            <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!70 to 74 years`                                                     <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!70 to 74 years`                                          <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!70 to 74 years`                                                        <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!70 to 74 years`                                             <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!70 to 74 years`                                                <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!70 to 74 years`                                     <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!70 to 74 years`                                                      <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!70 to 74 years`                                           <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!70 to 74 years`                                              <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!70 to 74 years`                                   <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!75 to 79 years`                                                       <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!75 to 79 years`                                            <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!75 to 79 years`                                                     <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!75 to 79 years`                                          <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!75 to 79 years`                                                        <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!75 to 79 years`                                             <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!75 to 79 years`                                                <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!75 to 79 years`                                     <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!75 to 79 years`                                                      <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!75 to 79 years`                                           <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!75 to 79 years`                                              <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!75 to 79 years`                                   <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!80 to 84 years`                                                       <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!80 to 84 years`                                            <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!80 to 84 years`                                                     <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!80 to 84 years`                                          <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!80 to 84 years`                                                        <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!80 to 84 years`                                             <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!80 to 84 years`                                                <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!80 to 84 years`                                     <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!80 to 84 years`                                                      <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!80 to 84 years`                                           <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!80 to 84 years`                                              <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!80 to 84 years`                                   <dbl> …
## $ `Estimate!!Total!!Total population!!AGE!!85 years and over`                                                    <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!AGE!!85 years and over`                                         <dbl> …
## $ `Estimate!!Percent!!Total population!!AGE!!85 years and over`                                                  <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!AGE!!85 years and over`                                       <dbl> …
## $ `Estimate!!Male!!Total population!!AGE!!85 years and over`                                                     <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!AGE!!85 years and over`                                          <dbl> …
## $ `Estimate!!Percent Male!!Total population!!AGE!!85 years and over`                                             <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!AGE!!85 years and over`                                  <dbl> …
## $ `Estimate!!Female!!Total population!!AGE!!85 years and over`                                                   <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!AGE!!85 years and over`                                        <dbl> …
## $ `Estimate!!Percent Female!!Total population!!AGE!!85 years and over`                                           <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!AGE!!85 years and over`                                <dbl> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years`                                    <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years`                         <chr> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years`                                  <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years`                       <chr> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years`                                     <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years`                          <chr> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years`                             <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years`                  <chr> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years`                                   <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years`                        <chr> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years`                           <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!5 to 14 years`                <chr> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years`                                   <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years`                        <chr> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years`                                 <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years`                      <chr> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years`                                    <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years`                         <chr> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years`                            <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years`                 <chr> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years`                                  <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years`                       <chr> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years`                          <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 17 years`               <chr> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years`                                   <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years`                        <chr> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years`                                 <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years`                      <chr> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years`                                    <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years`                         <chr> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years`                            <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years`                 <chr> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years`                                  <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years`                       <chr> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years`                          <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years`               <chr> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years`                                   <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years`                        <chr> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years`                                 <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years`                      <chr> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years`                                    <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years`                         <chr> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years`                            <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years`                 <chr> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years`                                  <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years`                       <chr> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years`                          <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years`               <chr> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years`                                   <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years`                        <chr> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years`                                 <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years`                      <chr> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years`                                    <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years`                         <chr> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years`                            <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years`                 <chr> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years`                                  <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years`                       <chr> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years`                          <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years`               <chr> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!16 years and over`                                <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!16 years and over`                     <dbl> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!16 years and over`                              <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!16 years and over`                   <dbl> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!16 years and over`                                 <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!16 years and over`                      <dbl> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!16 years and over`                         <dbl> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!16 years and over`                               <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!16 years and over`                    <dbl> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!16 years and over`                       <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!16 years and over`            <dbl> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!18 years and over`                                <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!18 years and over`                     <chr> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!18 years and over`                              <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!18 years and over`                   <chr> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!18 years and over`                                 <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!18 years and over`                      <chr> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!18 years and over`                         <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!18 years and over`              <chr> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!18 years and over`                               <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!18 years and over`                    <chr> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!18 years and over`                       <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!18 years and over`            <chr> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!21 years and over`                                <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!21 years and over`                     <dbl> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!21 years and over`                              <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!21 years and over`                   <dbl> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!21 years and over`                                 <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!21 years and over`                      <dbl> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!21 years and over`                         <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!21 years and over`              <dbl> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!21 years and over`                               <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!21 years and over`                    <dbl> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!21 years and over`                       <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!21 years and over`            <dbl> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!60 years and over`                                <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!60 years and over`                     <dbl> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!60 years and over`                              <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!60 years and over`                   <dbl> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!60 years and over`                                 <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!60 years and over`                      <dbl> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!60 years and over`                         <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!60 years and over`              <dbl> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!60 years and over`                               <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!60 years and over`                    <dbl> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!60 years and over`                       <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!60 years and over`            <dbl> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!62 years and over`                                <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!62 years and over`                     <dbl> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!62 years and over`                              <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!62 years and over`                   <dbl> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!62 years and over`                                 <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!62 years and over`                      <dbl> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!62 years and over`                         <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!62 years and over`              <dbl> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!62 years and over`                               <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!62 years and over`                    <dbl> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!62 years and over`                       <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!62 years and over`            <dbl> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!65 years and over`                                <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!65 years and over`                     <chr> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!65 years and over`                              <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!65 years and over`                   <chr> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!65 years and over`                                 <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!65 years and over`                      <chr> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!65 years and over`                         <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!65 years and over`              <chr> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!65 years and over`                               <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!65 years and over`                    <chr> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!65 years and over`                       <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!65 years and over`            <chr> …
## $ `Estimate!!Total!!Total population!!SELECTED AGE CATEGORIES!!75 years and over`                                <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SELECTED AGE CATEGORIES!!75 years and over`                     <chr> …
## $ `Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!75 years and over`                              <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SELECTED AGE CATEGORIES!!75 years and over`                   <chr> …
## $ `Estimate!!Male!!Total population!!SELECTED AGE CATEGORIES!!75 years and over`                                 <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SELECTED AGE CATEGORIES!!75 years and over`                      <chr> …
## $ `Estimate!!Percent Male!!Total population!!SELECTED AGE CATEGORIES!!75 years and over`                         <dbl> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!75 years and over`              <chr> …
## $ `Estimate!!Female!!Total population!!SELECTED AGE CATEGORIES!!75 years and over`                               <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SELECTED AGE CATEGORIES!!75 years and over`                    <chr> …
## $ `Estimate!!Percent Female!!Total population!!SELECTED AGE CATEGORIES!!75 years and over`                       <dbl> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SELECTED AGE CATEGORIES!!75 years and over`            <chr> …
## $ `Estimate!!Total!!Total population!!SUMMARY INDICATORS!!Median age (years)`                                    <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SUMMARY INDICATORS!!Median age (years)`                         <dbl> …
## $ `Estimate!!Percent!!Total population!!SUMMARY INDICATORS!!Median age (years)`                                  <chr> …
## $ `Margin of Error!!Percent MOE!!Total population!!SUMMARY INDICATORS!!Median age (years)`                       <chr> …
## $ `Estimate!!Male!!Total population!!SUMMARY INDICATORS!!Median age (years)`                                     <dbl> …
## $ `Margin of Error!!Male MOE!!Total population!!SUMMARY INDICATORS!!Median age (years)`                          <dbl> …
## $ `Estimate!!Percent Male!!Total population!!SUMMARY INDICATORS!!Median age (years)`                             <chr> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SUMMARY INDICATORS!!Median age (years)`                  <chr> …
## $ `Estimate!!Female!!Total population!!SUMMARY INDICATORS!!Median age (years)`                                   <dbl> …
## $ `Margin of Error!!Female MOE!!Total population!!SUMMARY INDICATORS!!Median age (years)`                        <dbl> …
## $ `Estimate!!Percent Female!!Total population!!SUMMARY INDICATORS!!Median age (years)`                           <chr> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SUMMARY INDICATORS!!Median age (years)`                <chr> …
## $ `Estimate!!Total!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)`                     <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)`          <chr> …
## $ `Estimate!!Percent!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)`                   <chr> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SELECTED AGE CATEGORIES!!16 years and over`              <dbl> …
## $ `Margin of Error!!Percent MOE!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)`        <chr> …
## $ `Estimate!!Male!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)`                      <chr> …
## $ `Margin of Error!!Male MOE!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)`           <chr> …
## $ `Estimate!!Percent Male!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)`              <chr> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)`   <chr> …
## $ `Estimate!!Female!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)`                    <chr> …
## $ `Margin of Error!!Female MOE!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)`         <chr> …
## $ `Estimate!!Percent Female!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)`            <chr> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SUMMARY INDICATORS!!Sex ratio (males per 100 females)` <chr> …
## $ `Estimate!!Total!!Total population!!SUMMARY INDICATORS!!Age dependency ratio`                                  <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SUMMARY INDICATORS!!Age dependency ratio`                       <chr> …
## $ `Estimate!!Percent!!Total population!!SUMMARY INDICATORS!!Age dependency ratio`                                <chr> …
## $ `Estimate!!Female!!Total population!!SUMMARY INDICATORS!!Child dependency ratio`                               <chr> …
## $ `Margin of Error!!Female MOE!!Total population!!SUMMARY INDICATORS!!Child dependency ratio`                    <chr> …
## $ `Estimate!!Percent Female!!Total population!!SUMMARY INDICATORS!!Child dependency ratio`                       <chr> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SUMMARY INDICATORS!!Child dependency ratio`            <chr> …
## $ `Estimate!!Total!!PERCENT ALLOCATED!!Sex`                                                                      <chr> …
## $ `Margin of Error!!Total MOE!!PERCENT ALLOCATED!!Sex`                                                           <chr> …
## $ `Estimate!!Percent!!PERCENT ALLOCATED!!Sex`                                                                    <dbl> …
## $ `Margin of Error!!Percent MOE!!PERCENT ALLOCATED!!Sex`                                                         <chr> …
## $ `Estimate!!Male!!PERCENT ALLOCATED!!Sex`                                                                       <chr> …
## $ `Margin of Error!!Male MOE!!PERCENT ALLOCATED!!Sex`                                                            <chr> …
## $ `Estimate!!Percent Male!!PERCENT ALLOCATED!!Sex`                                                               <chr> …
## $ `Margin of Error!!Percent Male MOE!!PERCENT ALLOCATED!!Sex`                                                    <chr> …
## $ `Margin of Error!!Percent MOE!!Total population!!SUMMARY INDICATORS!!Age dependency ratio`                     <chr> …
## $ `Estimate!!Male!!Total population!!SUMMARY INDICATORS!!Age dependency ratio`                                   <chr> …
## $ `Margin of Error!!Male MOE!!Total population!!SUMMARY INDICATORS!!Age dependency ratio`                        <chr> …
## $ `Estimate!!Percent Male!!Total population!!SUMMARY INDICATORS!!Age dependency ratio`                           <chr> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SUMMARY INDICATORS!!Age dependency ratio`                <chr> …
## $ `Estimate!!Female!!Total population!!SUMMARY INDICATORS!!Age dependency ratio`                                 <chr> …
## $ `Margin of Error!!Female MOE!!Total population!!SUMMARY INDICATORS!!Age dependency ratio`                      <chr> …
## $ `Estimate!!Percent Female!!Total population!!SUMMARY INDICATORS!!Age dependency ratio`                         <chr> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SUMMARY INDICATORS!!Age dependency ratio`              <chr> …
## $ `Estimate!!Total!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio`                              <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio`                   <chr> …
## $ `Estimate!!Percent!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio`                            <chr> …
## $ `Margin of Error!!Percent MOE!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio`                 <chr> …
## $ `Estimate!!Male!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio`                               <chr> …
## $ `Margin of Error!!Male MOE!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio`                    <chr> …
## $ `Estimate!!Percent Male!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio`                       <chr> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio`            <chr> …
## $ `Estimate!!Female!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio`                             <chr> …
## $ `Margin of Error!!Female MOE!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio`                  <chr> …
## $ `Estimate!!Percent Female!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio`                     <chr> …
## $ `Margin of Error!!Percent Female MOE!!Total population!!SUMMARY INDICATORS!!Old-age dependency ratio`          <chr> …
## $ `Estimate!!Total!!Total population!!SUMMARY INDICATORS!!Child dependency ratio`                                <dbl> …
## $ `Margin of Error!!Total MOE!!Total population!!SUMMARY INDICATORS!!Child dependency ratio`                     <chr> …
## $ `Estimate!!Percent!!Total population!!SUMMARY INDICATORS!!Child dependency ratio`                              <chr> …
## $ `Margin of Error!!Percent MOE!!Total population!!SUMMARY INDICATORS!!Child dependency ratio`                   <chr> …
## $ `Estimate!!Male!!Total population!!SUMMARY INDICATORS!!Child dependency ratio`                                 <chr> …
## $ `Margin of Error!!Male MOE!!Total population!!SUMMARY INDICATORS!!Child dependency ratio`                      <chr> …
## $ `Estimate!!Percent Male!!Total population!!SUMMARY INDICATORS!!Child dependency ratio`                         <chr> …
## $ `Margin of Error!!Percent Male MOE!!Total population!!SUMMARY INDICATORS!!Child dependency ratio`              <chr> …
## $ `Estimate!!Female!!PERCENT ALLOCATED!!Sex`                                                                     <chr> …
## $ `Margin of Error!!Female MOE!!PERCENT ALLOCATED!!Sex`                                                          <chr> …
## $ `Estimate!!Percent Female!!PERCENT ALLOCATED!!Sex`                                                             <chr> …
## $ `Margin of Error!!Percent Female MOE!!PERCENT ALLOCATED!!Sex`                                                  <chr> …
## $ `Estimate!!Total!!PERCENT ALLOCATED!!Age`                                                                      <chr> …
## $ `Margin of Error!!Total MOE!!PERCENT ALLOCATED!!Age`                                                           <chr> …
## $ `Estimate!!Percent!!PERCENT ALLOCATED!!Age`                                                                    <dbl> …
## $ `Margin of Error!!Percent MOE!!PERCENT ALLOCATED!!Age`                                                         <chr> …
## $ `Estimate!!Male!!PERCENT ALLOCATED!!Age`                                                                       <chr> …
## $ `Margin of Error!!Male MOE!!PERCENT ALLOCATED!!Age`                                                            <chr> …
## $ `Estimate!!Percent Male!!PERCENT ALLOCATED!!Age`                                                               <chr> …
## $ `Margin of Error!!Percent Male MOE!!PERCENT ALLOCATED!!Age`                                                    <chr> …
## $ `Estimate!!Female!!PERCENT ALLOCATED!!Age`                                                                     <chr> …
## $ `Margin of Error!!Female MOE!!PERCENT ALLOCATED!!Age`                                                          <chr> …
## $ `Estimate!!Percent Female!!PERCENT ALLOCATED!!Age`                                                             <chr> …
## $ `Margin of Error!!Percent Female MOE!!PERCENT ALLOCATED!!Age`                                                  <chr> …

Import problems

problems(acs5_raw)

LAD Data Import

These data will be imported from a previously downloaded copy of the data file that was provided by the US Census Bureau as an XLS file. To read this file we have to have previously loaded the readxl library into our R session.

# relative path and filename for the xls file to be imported
lad_filepath <- "data/LandArea/LND01.xls"
lad_raw <- read_excel(lad_filepath)

Column specifications

glimpse(lad_raw)
## Observations: 3,198
## Variables: 34
## $ Areaname    <chr> "UNITED STATES", "ALABAMA", "Autauga, AL", "Baldwin, AL",…
## $ STCOU       <chr> "00000", "01000", "01001", "01003", "01005", "01007", "01…
## $ LND010190F  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LND010190D  <dbl> 3787425.1, 52422.9, 604.5, 2027.1, 904.6, 625.5, 650.6, 6…
## $ LND010190N1 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND010190N2 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND010200F  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LND010200D  <dbl> 3794083.1, 52419.0, 604.5, 2026.9, 904.5, 626.2, 650.6, 6…
## $ LND010200N1 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND010200N2 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND110180F  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LND110180D  <dbl> 3539289.2, 50767.2, 597.0, 1589.4, 883.9, 625.0, 643.3, 6…
## $ LND110180N1 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND110180N2 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND110190F  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LND110190D  <dbl> 3536341.7, 50750.2, 596.0, 1596.5, 885.0, 622.4, 645.7, 6…
## $ LND110190N1 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND110190N2 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND110200F  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LND110200D  <dbl> 3537438.4, 50744.0, 596.0, 1596.3, 884.9, 623.0, 645.6, 6…
## $ LND110200N1 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND110200N2 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND110210F  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LND110210D  <dbl> 3531905.4, 50645.3, 594.4, 1589.8, 884.9, 622.6, 644.8, 6…
## $ LND110210N1 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND110210N2 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND210190F  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LND210190D  <dbl> 251083.35, 1672.71, 8.48, 430.55, 19.59, 3.14, 4.97, 1.04…
## $ LND210190N1 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND210190N2 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND210200F  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ LND210200D  <dbl> 256644.62, 1675.01, 8.48, 430.58, 19.61, 3.14, 5.02, 1.04…
## $ LND210200N1 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…
## $ LND210200N2 <chr> "0000", "0000", "0000", "0000", "0000", "0000", "0000", "…

Import problems

problems(lad_raw)

C19 data import

These data are going to be directly downloaded from the github repository that the Johns Hopkins CSSE updates on a regular basis. By redownloading the current data from the repository our analysis will always reflect the current state of knowledge about infections for each county in the database.

# relative path and filename for the xls file to be imported
c19_filepath <- "https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv"
c19_raw <- read_csv(c19_filepath)

Column specifications

glimpse(c19_raw)
## Observations: 3,340
## Variables: 410
## $ UID            <dbl> 84001001, 84001003, 84001005, 84001007, 84001009, 8400…
## $ iso2           <chr> "US", "US", "US", "US", "US", "US", "US", "US", "US", …
## $ iso3           <chr> "USA", "USA", "USA", "USA", "USA", "USA", "USA", "USA"…
## $ code3          <dbl> 840, 840, 840, 840, 840, 840, 840, 840, 840, 840, 840,…
## $ FIPS           <dbl> 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 1017, …
## $ Admin2         <chr> "Autauga", "Baldwin", "Barbour", "Bibb", "Blount", "Bu…
## $ Province_State <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",…
## $ Country_Region <chr> "US", "US", "US", "US", "US", "US", "US", "US", "US", …
## $ Lat            <dbl> 32.54, 30.73, 31.87, 33.00, 33.98, 32.10, 31.75, 33.77…
## $ Long_          <dbl> -86.64, -87.72, -85.39, -87.13, -86.57, -85.71, -86.68…
## $ Combined_Key   <chr> "Autauga, Alabama, US", "Baldwin, Alabama, US", "Barbo…
## $ `1/22/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/23/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/24/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/25/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/26/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/27/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/28/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/29/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/30/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `1/31/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/1/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/2/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/3/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/4/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/5/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/6/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/7/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/8/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/9/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/10/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/11/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/12/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/13/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/14/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/15/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/16/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/17/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/18/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/19/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/20/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/21/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/22/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/23/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/24/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/25/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/26/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/27/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/28/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `2/29/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/1/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/2/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/3/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/4/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/5/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/6/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/7/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/8/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/9/20`       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/10/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/11/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/12/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/13/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/14/20`      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/15/20`      <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/16/20`      <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/17/20`      <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/18/20`      <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/19/20`      <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/20/20`      <dbl> 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/21/20`      <dbl> 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/22/20`      <dbl> 0, 2, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/23/20`      <dbl> 0, 3, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/24/20`      <dbl> 1, 5, 0, 0, 0, 0, 0, 2, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `3/25/20`      <dbl> 5, 5, 0, 0, 3, 0, 1, 2, 9, 1, 4, 0, 0, 1, 1, 0, 1, 0, …
## $ `3/26/20`      <dbl> 6, 6, 0, 0, 4, 1, 1, 2, 13, 1, 4, 1, 0, 1, 1, 0, 1, 0,…
## $ `3/27/20`      <dbl> 6, 6, 0, 2, 6, 1, 1, 4, 15, 1, 6, 1, 0, 1, 3, 0, 1, 0,…
## $ `3/28/20`      <dbl> 6, 18, 0, 2, 6, 2, 1, 4, 22, 1, 7, 1, 0, 2, 4, 0, 2, 0…
## $ `3/29/20`      <dbl> 6, 19, 0, 2, 7, 2, 1, 4, 29, 1, 8, 1, 0, 2, 5, 0, 2, 0…
## $ `3/30/20`      <dbl> 8, 22, 0, 3, 7, 2, 1, 9, 39, 1, 10, 2, 0, 2, 5, 0, 4, …
## $ `3/31/20`      <dbl> 8, 23, 0, 3, 7, 2, 1, 10, 40, 1, 11, 4, 0, 2, 5, 0, 4,…
## $ `4/1/20`       <dbl> 10, 26, 0, 3, 7, 2, 1, 11, 51, 1, 13, 5, 2, 3, 6, 1, 4…
## $ `4/2/20`       <dbl> 12, 29, 0, 4, 9, 2, 1, 12, 75, 3, 14, 5, 2, 7, 6, 4, 5…
## $ `4/3/20`       <dbl> 12, 31, 2, 4, 11, 2, 1, 22, 87, 4, 15, 5, 3, 8, 7, 7, …
## $ `4/4/20`       <dbl> 12, 34, 3, 4, 12, 2, 1, 23, 91, 5, 15, 5, 7, 9, 7, 8, …
## $ `4/5/20`       <dbl> 12, 39, 3, 7, 13, 2, 1, 26, 94, 5, 18, 6, 9, 9, 7, 8, …
## $ `4/6/20`       <dbl> 12, 43, 4, 7, 13, 2, 1, 40, 102, 5, 20, 8, 9, 9, 9, 9,…
## $ `4/7/20`       <dbl> 12, 47, 4, 8, 13, 2, 2, 51, 112, 5, 21, 8, 10, 9, 12, …
## $ `4/8/20`       <dbl> 12, 54, 4, 9, 13, 3, 3, 54, 145, 7, 23, 8, 10, 11, 12,…
## $ `4/9/20`       <dbl> 17, 63, 8, 11, 15, 4, 3, 56, 170, 7, 26, 8, 13, 11, 12…
## $ `4/10/20`      <dbl> 18, 64, 9, 11, 15, 4, 3, 56, 178, 7, 29, 10, 13, 11, 1…
## $ `4/11/20`      <dbl> 19, 70, 10, 13, 15, 4, 6, 59, 189, 7, 31, 11, 15, 12, …
## $ `4/12/20`      <dbl> 19, 75, 10, 16, 17, 4, 7, 63, 206, 9, 31, 12, 18, 14, …
## $ `4/13/20`      <dbl> 19, 82, 10, 17, 18, 6, 8, 64, 215, 9, 35, 12, 18, 14, …
## $ `4/14/20`      <dbl> 23, 93, 11, 18, 19, 8, 8, 65, 219, 9, 35, 14, 18, 14, …
## $ `4/15/20`      <dbl> 24, 100, 13, 19, 21, 8, 11, 66, 229, 10, 38, 15, 19, 1…
## $ `4/16/20`      <dbl> 24, 104, 14, 23, 22, 8, 11, 68, 234, 11, 38, 15, 21, 1…
## $ `4/17/20`      <dbl> 24, 106, 15, 23, 22, 8, 13, 68, 239, 11, 39, 15, 21, 1…
## $ `4/18/20`      <dbl> 25, 110, 18, 26, 24, 9, 14, 71, 242, 12, 41, 16, 21, 1…
## $ `4/19/20`      <dbl> 26, 116, 20, 26, 25, 9, 15, 78, 247, 12, 45, 16, 21, 1…
## $ `4/20/20`      <dbl> 28, 120, 22, 30, 27, 11, 15, 82, 257, 13, 45, 18, 22, …
## $ `4/21/20`      <dbl> 30, 126, 28, 30, 31, 11, 15, 85, 259, 14, 45, 18, 22, …
## $ `4/22/20`      <dbl> 32, 134, 29, 32, 33, 11, 17, 87, 266, 14, 47, 21, 22, …
## $ `4/23/20`      <dbl> 33, 143, 31, 32, 35, 12, 19, 89, 273, 14, 49, 23, 23, …
## $ `4/24/20`      <dbl> 36, 148, 33, 33, 36, 12, 21, 89, 277, 14, 51, 23, 23, …
## $ `4/25/20`      <dbl> 36, 155, 34, 34, 36, 12, 28, 90, 279, 14, 52, 25, 24, …
## $ `4/26/20`      <dbl> 36, 161, 34, 37, 38, 12, 32, 91, 281, 15, 53, 30, 25, …
## $ `4/27/20`      <dbl> 37, 167, 35, 40, 38, 12, 34, 91, 284, 15, 53, 36, 26, …
## $ `4/28/20`      <dbl> 39, 172, 38, 40, 38, 12, 48, 94, 286, 16, 54, 37, 27, …
## $ `4/29/20`      <dbl> 41, 174, 38, 40, 39, 12, 53, 95, 288, 16, 54, 37, 30, …
## $ `4/30/20`      <dbl> 42, 180, 38, 40, 39, 13, 55, 97, 292, 16, 55, 44, 31, …
## $ `5/1/20`       <dbl> 43, 180, 42, 41, 41, 14, 68, 97, 295, 16, 55, 50, 33, …
## $ `5/2/20`       <dbl> 47, 185, 43, 41, 42, 14, 93, 102, 296, 16, 56, 50, 37,…
## $ `5/3/20`       <dbl> 51, 190, 45, 42, 42, 14, 104, 109, 302, 19, 59, 50, 41…
## $ `5/4/20`       <dbl> 54, 190, 45, 43, 42, 15, 113, 109, 307, 19, 61, 54, 41…
## $ `5/5/20`       <dbl> 54, 194, 47, 43, 42, 17, 118, 118, 309, 19, 64, 56, 42…
## $ `5/6/20`       <dbl> 56, 199, 47, 43, 44, 17, 128, 120, 313, 20, 64, 56, 44…
## $ `5/7/20`       <dbl> 58, 207, 50, 44, 46, 17, 152, 126, 317, 20, 69, 57, 48…
## $ `5/8/20`       <dbl> 62, 211, 52, 44, 47, 20, 159, 129, 320, 22, 69, 60, 51…
## $ `5/9/20`       <dbl> 63, 219, 57, 45, 47, 21, 175, 130, 323, 23, 70, 62, 51…
## $ `5/10/20`      <dbl> 72, 224, 59, 46, 47, 22, 188, 130, 325, 23, 72, 67, 55…
## $ `5/11/20`      <dbl> 81, 226, 61, 46, 48, 26, 196, 131, 327, 24, 74, 68, 62…
## $ `5/12/20`      <dbl> 88, 230, 67, 46, 48, 26, 223, 132, 332, 24, 76, 70, 64…
## $ `5/13/20`      <dbl> 90, 237, 70, 46, 48, 27, 231, 133, 333, 24, 76, 73, 65…
## $ `5/14/20`      <dbl> 100, 247, 74, 47, 48, 27, 244, 134, 334, 24, 78, 77, 6…
## $ `5/15/20`      <dbl> 100, 253, 79, 51, 48, 32, 254, 135, 335, 25, 79, 81, 7…
## $ `5/16/20`      <dbl> 108, 261, 81, 52, 49, 35, 265, 136, 337, 26, 81, 84, 7…
## $ `5/17/20`      <dbl> 118, 267, 85, 52, 49, 39, 278, 139, 337, 27, 83, 85, 7…
## $ `5/18/20`      <dbl> 124, 267, 89, 53, 50, 50, 289, 139, 338, 28, 84, 89, 7…
## $ `5/19/20`      <dbl> 130, 269, 92, 53, 50, 58, 297, 142, 339, 30, 85, 124, …
## $ `5/20/20`      <dbl> 135, 277, 96, 54, 50, 64, 306, 142, 342, 30, 87, 127, …
## $ `5/21/20`      <dbl> 148, 277, 101, 54, 51, 71, 315, 143, 343, 31, 88, 132,…
## $ `5/22/20`      <dbl> 151, 278, 106, 56, 52, 95, 325, 145, 343, 32, 89, 135,…
## $ `5/23/20`      <dbl> 156, 280, 108, 59, 52, 103, 329, 145, 343, 32, 90, 139…
## $ `5/24/20`      <dbl> 160, 281, 113, 60, 52, 109, 338, 148, 349, 32, 91, 140…
## $ `5/25/20`      <dbl> 171, 284, 116, 63, 52, 138, 360, 153, 350, 33, 91, 143…
## $ `5/26/20`      <dbl> 191, 285, 128, 68, 54, 167, 375, 157, 353, 33, 95, 146…
## $ `5/27/20`      <dbl> 192, 289, 133, 72, 56, 175, 387, 159, 356, 33, 99, 146…
## $ `5/28/20`      <dbl> 204, 289, 136, 72, 61, 185, 389, 159, 364, 34, 102, 14…
## $ `5/29/20`      <dbl> 211, 290, 151, 72, 63, 201, 394, 160, 367, 36, 104, 14…
## $ `5/30/20`      <dbl> 216, 292, 155, 74, 65, 205, 403, 161, 369, 37, 104, 15…
## $ `5/31/20`      <dbl> 227, 298, 172, 77, 66, 209, 413, 169, 370, 38, 105, 15…
## $ `6/1/20`       <dbl> 237, 299, 177, 78, 66, 211, 414, 172, 373, 38, 106, 15…
## $ `6/2/20`       <dbl> 239, 301, 181, 78, 66, 215, 418, 172, 374, 38, 108, 15…
## $ `6/3/20`       <dbl> 241, 302, 181, 78, 66, 217, 420, 174, 375, 38, 111, 15…
## $ `6/4/20`       <dbl> 248, 305, 187, 78, 67, 219, 430, 176, 379, 38, 112, 15…
## $ `6/5/20`       <dbl> 259, 312, 194, 79, 73, 225, 441, 181, 389, 40, 112, 15…
## $ `6/6/20`       <dbl> 264, 321, 196, 79, 76, 231, 447, 183, 393, 42, 115, 15…
## $ `6/7/20`       <dbl> 271, 327, 200, 81, 78, 237, 453, 184, 398, 42, 115, 15…
## $ `6/8/20`       <dbl> 282, 332, 201, 87, 80, 242, 462, 186, 407, 42, 121, 15…
## $ `6/9/20`       <dbl> 295, 338, 210, 91, 84, 247, 469, 188, 418, 43, 124, 15…
## $ `6/10/20`      <dbl> 315, 350, 215, 95, 92, 252, 482, 190, 434, 43, 127, 16…
## $ `6/11/20`      <dbl> 323, 357, 220, 97, 98, 256, 498, 193, 443, 46, 128, 16…
## $ `6/12/20`      <dbl> 334, 365, 226, 102, 106, 275, 517, 197, 457, 48, 134, …
## $ `6/13/20`      <dbl> 361, 367, 233, 106, 114, 300, 534, 201, 474, 52, 138, …
## $ `6/14/20`      <dbl> 369, 375, 237, 113, 120, 305, 542, 202, 486, 52, 143, …
## $ `6/15/20`      <dbl> 371, 377, 243, 113, 121, 306, 545, 203, 488, 52, 143, …
## $ `6/16/20`      <dbl> 377, 379, 252, 116, 125, 310, 548, 206, 491, 53, 151, …
## $ `6/17/20`      <dbl> 404, 389, 264, 119, 135, 316, 560, 208, 503, 54, 154, …
## $ `6/18/20`      <dbl> 415, 402, 267, 125, 140, 317, 562, 208, 507, 57, 157, …
## $ `6/19/20`      <dbl> 435, 408, 272, 125, 144, 324, 563, 209, 515, 57, 162, …
## $ `6/20/20`      <dbl> 438, 418, 272, 126, 151, 324, 567, 211, 520, 57, 165, …
## $ `6/21/20`      <dbl> 447, 426, 277, 128, 155, 325, 569, 213, 527, 57, 170, …
## $ `6/22/20`      <dbl> 458, 439, 280, 134, 162, 327, 572, 214, 533, 57, 176, …
## $ `6/23/20`      <dbl> 474, 453, 288, 141, 170, 330, 574, 223, 540, 58, 184, …
## $ `6/24/20`      <dbl> 480, 466, 305, 148, 177, 339, 580, 233, 547, 62, 184, …
## $ `6/25/20`      <dbl> 493, 505, 313, 161, 186, 340, 586, 236, 558, 66, 192, …
## $ `6/26/20`      <dbl> 500, 549, 317, 164, 192, 346, 593, 245, 560, 67, 199, …
## $ `6/27/20`      <dbl> 505, 573, 317, 164, 192, 346, 595, 245, 560, 69, 200, …
## $ `6/28/20`      <dbl> 528, 628, 324, 167, 200, 354, 598, 261, 581, 72, 208, …
## $ `6/29/20`      <dbl> 538, 668, 327, 172, 209, 354, 599, 276, 590, 73, 211, …
## $ `6/30/20`      <dbl> 554, 691, 328, 176, 218, 356, 599, 278, 594, 76, 218, …
## $ `7/1/20`       <dbl> 562, 739, 337, 181, 221, 358, 602, 288, 609, 82, 221, …
## $ `7/2/20`       <dbl> 570, 831, 352, 191, 232, 363, 614, 330, 620, 89, 234, …
## $ `7/3/20`       <dbl> 584, 847, 354, 193, 236, 363, 615, 341, 631, 89, 244, …
## $ `7/4/20`       <dbl> 601, 861, 356, 196, 240, 363, 624, 363, 634, 101, 251,…
## $ `7/5/20`       <dbl> 620, 893, 358, 198, 249, 364, 624, 375, 643, 102, 258,…
## $ `7/6/20`       <dbl> 623, 979, 365, 202, 257, 365, 625, 390, 652, 106, 264,…
## $ `7/7/20`       <dbl> 656, 1042, 372, 204, 270, 366, 630, 412, 658, 115, 281…
## $ `7/8/20`       <dbl> 663, 1118, 377, 214, 289, 366, 632, 443, 672, 115, 292…
## $ `7/9/20`       <dbl> 670, 1175, 387, 219, 301, 372, 639, 460, 678, 116, 300…
## $ `7/10/20`      <dbl> 691, 1215, 404, 226, 316, 373, 645, 496, 690, 126, 329…
## $ `7/11/20`      <dbl> 708, 1280, 409, 229, 338, 373, 646, 520, 694, 128, 337…
## $ `7/12/20`      <dbl> 736, 1349, 419, 232, 354, 374, 650, 562, 703, 135, 353…
## $ `7/13/20`      <dbl> 749, 1396, 437, 237, 368, 376, 651, 580, 713, 139, 364…
## $ `7/14/20`      <dbl> 764, 1506, 446, 243, 390, 377, 655, 649, 719, 144, 377…
## $ `7/15/20`      <dbl> 785, 1588, 462, 248, 426, 380, 659, 663, 732, 149, 394…
## $ `7/16/20`      <dbl> 797, 1675, 466, 255, 445, 383, 662, 706, 743, 155, 413…
## $ `7/17/20`      <dbl> 822, 1806, 483, 262, 464, 389, 668, 728, 756, 160, 431…
## $ `7/18/20`      <dbl> 850, 1925, 497, 270, 487, 390, 676, 772, 762, 171, 469…
## $ `7/19/20`      <dbl> 862, 1996, 504, 277, 510, 392, 678, 813, 767, 177, 482…
## $ `7/20/20`      <dbl> 872, 2085, 513, 283, 526, 393, 685, 849, 774, 181, 498…
## $ `7/21/20`      <dbl> 885, 2169, 520, 288, 549, 396, 693, 870, 781, 183, 516…
## $ `7/22/20`      <dbl> 905, 2344, 531, 290, 582, 401, 699, 893, 789, 192, 538…
## $ `7/23/20`      <dbl> 918, 2482, 540, 304, 616, 405, 705, 974, 796, 205, 563…
## $ `7/24/20`      <dbl> 939, 2610, 552, 318, 639, 410, 712, 1025, 810, 207, 58…
## $ `7/25/20`      <dbl> 953, 2675, 563, 324, 654, 411, 718, 1086, 820, 209, 60…
## $ `7/26/20`      <dbl> 971, 2733, 570, 335, 674, 412, 724, 1163, 824, 220, 62…
## $ `7/27/20`      <dbl> 988, 2800, 576, 339, 679, 425, 728, 1206, 835, 221, 64…
## $ `7/28/20`      <dbl> 995, 2873, 585, 345, 701, 427, 734, 1260, 844, 227, 66…
## $ `7/29/20`      <dbl> 1006, 3001, 586, 353, 738, 431, 740, 1305, 848, 235, 6…
## $ `7/30/20`      <dbl> 1029, 3075, 598, 364, 773, 433, 746, 1412, 859, 238, 6…
## $ `7/31/20`      <dbl> 1042, 3116, 603, 369, 799, 439, 749, 1445, 862, 242, 7…
## $ `8/1/20`       <dbl> 1064, 3194, 611, 373, 820, 441, 756, 1541, 869, 254, 7…
## $ `8/2/20`       <dbl> 1078, 3241, 613, 383, 838, 442, 758, 1570, 876, 262, 7…
## $ `8/3/20`       <dbl> 1086, 3298, 615, 390, 841, 445, 758, 1608, 882, 268, 7…
## $ `8/4/20`       <dbl> 1086, 3347, 616, 393, 845, 448, 761, 1645, 887, 270, 7…
## $ `8/5/20`       <dbl> 1109, 3409, 621, 420, 883, 454, 763, 1704, 894, 281, 7…
## $ `8/6/20`       <dbl> 1126, 3473, 627, 424, 917, 458, 766, 1744, 900, 291, 8…
## $ `8/7/20`       <dbl> 1145, 3533, 629, 434, 929, 467, 766, 1795, 905, 298, 8…
## $ `8/8/20`       <dbl> 1175, 3575, 632, 446, 945, 468, 771, 1821, 906, 301, 8…
## $ `8/9/20`       <dbl> 1186, 3676, 633, 450, 957, 470, 774, 1847, 909, 302, 8…
## $ `8/10/20`      <dbl> 1200, 3700, 642, 454, 971, 486, 775, 1874, 915, 306, 8…
## $ `8/11/20`      <dbl> 1224, 3749, 648, 465, 979, 497, 778, 1902, 918, 311, 8…
## $ `8/12/20`      <dbl> 1229, 3783, 653, 469, 994, 497, 780, 1925, 919, 319, 8…
## $ `8/13/20`      <dbl> 1235, 3825, 658, 475, 998, 498, 786, 1941, 922, 326, 8…
## $ `8/14/20`      <dbl> 1245, 3881, 662, 479, 1004, 500, 796, 1980, 925, 333, …
## $ `8/15/20`      <dbl> 1252, 3923, 672, 481, 1014, 500, 799, 2000, 927, 337, …
## $ `8/16/20`      <dbl> 1258, 3936, 673, 485, 1023, 501, 803, 2015, 928, 341, …
## $ `8/17/20`      <dbl> 1276, 3959, 675, 487, 1060, 512, 803, 2061, 937, 350, …
## $ `8/18/20`      <dbl> 1281, 3994, 682, 501, 1086, 530, 805, 2121, 943, 351, …
## $ `8/19/20`      <dbl> 1293, 4029, 692, 507, 1113, 535, 809, 2181, 949, 358, …
## $ `8/20/20`      <dbl> 1304, 4058, 694, 512, 1133, 536, 811, 2211, 965, 362, …
## $ `8/21/20`      <dbl> 1316, 4100, 710, 515, 1147, 539, 813, 2246, 969, 367, …
## $ `8/22/20`      <dbl> 1318, 4132, 712, 521, 1149, 539, 815, 2260, 970, 369, …
## $ `8/23/20`      <dbl> 1337, 4148, 715, 521, 1160, 540, 816, 2285, 970, 374, …
## $ `8/24/20`      <dbl> 1343, 4171, 720, 522, 1177, 540, 817, 2302, 973, 376, …
## $ `8/25/20`      <dbl> 1357, 4247, 728, 526, 1224, 541, 824, 2360, 982, 379, …
## $ `8/26/20`      <dbl> 1365, 4296, 736, 530, 1239, 542, 827, 2380, 1012, 387,…
## $ `8/27/20`      <dbl> 1375, 4330, 741, 532, 1251, 544, 829, 2409, 1015, 390,…
## $ `8/28/20`      <dbl> 1391, 4408, 749, 539, 1273, 551, 845, 2468, 1023, 395,…
## $ `8/29/20`      <dbl> 1424, 4502, 752, 550, 1301, 555, 855, 2500, 1024, 402,…
## $ `8/30/20`      <dbl> 1429, 4519, 752, 553, 1312, 556, 857, 2530, 1029, 403,…
## $ `8/31/20`      <dbl> 1440, 4538, 759, 558, 1332, 564, 865, 2582, 1037, 414,…
## $ `9/1/20`       <dbl> 1442, 4563, 763, 562, 1336, 566, 867, 2606, 1040, 419,…
## $ `9/2/20`       <dbl> 1454, 4599, 765, 564, 1356, 567, 868, 2648, 1041, 426,…
## $ `9/3/20`       <dbl> 1462, 4626, 770, 567, 1376, 567, 872, 2714, 1047, 440,…
## $ `9/4/20`       <dbl> 1474, 4654, 770, 571, 1392, 567, 875, 2786, 1053, 447,…
## $ `9/5/20`       <dbl> 1477, 4684, 771, 576, 1399, 569, 879, 2797, 1055, 449,…
## $ `9/6/20`       <dbl> 1488, 4700, 772, 578, 1416, 569, 881, 2836, 1058, 452,…
## $ `9/7/20`       <dbl> 1494, 4725, 772, 585, 1418, 569, 881, 2840, 1058, 452,…
## $ `9/8/20`       <dbl> 1505, 4755, 772, 586, 1424, 569, 882, 2859, 1061, 455,…
## $ `9/9/20`       <dbl> 1526, 4796, 779, 589, 1441, 571, 883, 2900, 1069, 465,…
## $ `9/10/20`      <dbl> 1530, 4845, 781, 593, 1453, 571, 884, 2922, 1079, 471,…
## $ `9/11/20`      <dbl> 1543, 4881, 787, 594, 1459, 572, 886, 2975, 1085, 482,…
## $ `9/12/20`      <dbl> 1551, 4915, 789, 596, 1472, 578, 888, 3023, 1085, 502,…
## $ `9/13/20`      <dbl> 1567, 4934, 796, 599, 1483, 578, 889, 3040, 1087, 504,…
## $ `9/14/20`      <dbl> 1586, 4949, 801, 600, 1490, 579, 890, 3066, 1092, 506,…
## $ `9/15/20`      <dbl> 1601, 4964, 807, 601, 1504, 581, 892, 3106, 1097, 510,…
## $ `9/16/20`      <dbl> 1614, 4982, 807, 606, 1515, 583, 893, 3156, 1097, 513,…
## $ `9/17/20`      <dbl> 1650, 4994, 822, 607, 1538, 583, 896, 3189, 1109, 522,…
## $ `9/18/20`      <dbl> 1659, 5016, 825, 619, 1551, 584, 897, 3268, 1114, 539,…
## $ `9/19/20`      <dbl> 1675, 5029, 831, 623, 1564, 586, 897, 3289, 1121, 549,…
## $ `9/20/20`      <dbl> 1676, 5053, 833, 624, 1573, 590, 898, 3321, 1123, 552,…
## $ `9/21/20`      <dbl> 1697, 5090, 846, 628, 1586, 592, 898, 3341, 1131, 557,…
## $ `9/22/20`      <dbl> 1697, 5106, 848, 633, 1593, 596, 899, 3353, 1135, 567,…
## $ `9/23/20`      <dbl> 1711, 5127, 852, 637, 1605, 597, 901, 3412, 1142, 574,…
## $ `9/24/20`      <dbl> 1736, 5397, 868, 646, 1614, 598, 903, 3450, 1151, 586,…
## $ `9/25/20`      <dbl> 1750, 5419, 878, 649, 1619, 603, 906, 3466, 1154, 592,…
## $ `9/26/20`      <dbl> 1758, 5465, 882, 650, 1623, 605, 907, 3485, 1157, 596,…
## $ `9/27/20`      <dbl> 1770, 5524, 883, 651, 1624, 606, 908, 3498, 1161, 597,…
## $ `9/28/20`      <dbl> 1776, 5550, 883, 653, 1626, 606, 911, 3516, 1164, 601,…
## $ `9/29/20`      <dbl> 1785, 5592, 892, 660, 1633, 608, 913, 3530, 1170, 605,…
## $ `9/30/20`      <dbl> 1792, 5954, 894, 668, 1636, 611, 915, 3556, 1172, 609,…
## $ `10/1/20`      <dbl> 1799, 5981, 900, 671, 1644, 611, 917, 3597, 1175, 616,…
## $ `10/2/20`      <dbl> 1812, 6009, 917, 675, 1656, 611, 919, 3629, 1195, 625,…
## $ `10/3/20`      <dbl> 1821, 6034, 917, 683, 1659, 611, 920, 3647, 1196, 625,…
## $ `10/4/20`      <dbl> 1824, 6045, 917, 683, 1664, 611, 921, 3672, 1199, 628,…
## $ `10/5/20`      <dbl> 1832, 6075, 918, 688, 1667, 614, 923, 3700, 1201, 630,…
## $ `10/6/20`      <dbl> 1843, 6103, 923, 700, 1678, 616, 926, 3718, 1203, 636,…
## $ `10/7/20`      <dbl> 1847, 6114, 925, 703, 1686, 618, 928, 3736, 1212, 640,…
## $ `10/8/20`      <dbl> 1875, 6144, 937, 715, 1700, 622, 937, 3790, 1231, 648,…
## $ `10/9/20`      <dbl> 1894, 6164, 940, 724, 1712, 623, 948, 3811, 1235, 653,…
## $ `10/10/20`     <dbl> 1901, 6176, 940, 734, 1720, 624, 954, 3829, 1235, 657,…
## $ `10/11/20`     <dbl> 1907, 6192, 941, 736, 1732, 625, 958, 3848, 1238, 661,…
## $ `10/12/20`     <dbl> 1921, 6222, 948, 741, 1755, 626, 964, 3884, 1247, 674,…
## $ `10/13/20`     <dbl> 1925, 6247, 948, 742, 1765, 628, 974, 3911, 1251, 677,…
## $ `10/14/20`     <dbl> 1946, 6266, 963, 759, 1782, 628, 979, 3939, 1255, 687,…
## $ `10/15/20`     <dbl> 1958, 6313, 966, 769, 1796, 630, 985, 3992, 1258, 694,…
## $ `10/16/20`     <dbl> 1971, 6332, 975, 773, 1822, 633, 987, 4053, 1261, 705,…
## $ `10/17/20`     <dbl> 1985, 6350, 978, 783, 1837, 633, 993, 4071, 1261, 706,…
## $ `10/18/20`     <dbl> 1995, 6356, 978, 787, 1848, 634, 996, 4090, 1268, 707,…
## $ `10/19/20`     <dbl> 2006, 6384, 984, 789, 1863, 635, 996, 4115, 1298, 714,…
## $ `10/20/20`     <dbl> 2018, 6425, 993, 799, 1887, 636, 996, 4143, 1328, 717,…
## $ `10/21/20`     <dbl> 2021, 6459, 1007, 809, 1907, 636, 999, 4175, 1334, 720…
## $ `10/22/20`     <dbl> 2027, 6599, 1010, 823, 1923, 638, 1000, 4213, 1341, 72…
## $ `10/23/20`     <dbl> 2040, 6619, 1028, 825, 1934, 647, 1005, 4553, 1346, 72…
## $ `10/24/20`     <dbl> 2055, 6642, 1030, 839, 1947, 648, 1009, 4587, 1347, 73…
## $ `10/25/20`     <dbl> 2070, 6677, 1030, 841, 1958, 648, 1011, 4602, 1349, 73…
## $ `10/26/20`     <dbl> 2079, 6694, 1038, 849, 1986, 649, 1011, 4634, 1365, 74…
## $ `10/27/20`     <dbl> 2098, 6728, 1042, 858, 2002, 649, 1014, 4675, 1369, 75…
## $ `10/28/20`     <dbl> 2120, 6757, 1052, 862, 2027, 650, 1018, 4757, 1379, 75…
## $ `10/29/20`     <dbl> 2134, 6879, 1053, 867, 2054, 651, 1018, 4800, 1381, 76…
## $ `10/30/20`     <dbl> 2154, 6931, 1058, 873, 2089, 651, 1021, 4855, 1389, 77…
## $ `10/31/20`     <dbl> 2168, 6955, 1059, 877, 2109, 653, 1023, 4883, 1392, 78…
## $ `11/1/20`      <dbl> 2182, 6974, 1062, 883, 2128, 653, 1025, 4913, 1397, 78…
## $ `11/2/20`      <dbl> 2195, 6991, 1073, 890, 2178, 656, 1028, 4946, 1428, 80…
## $ `11/3/20`      <dbl> 2210, 7054, 1077, 900, 2204, 658, 1032, 4996, 1449, 81…
## $ `11/4/20`      <dbl> 2229, 7093, 1079, 907, 2233, 660, 1035, 5030, 1461, 83…
## $ `11/5/20`      <dbl> 2244, 7133, 1089, 920, 2258, 662, 1043, 5072, 1469, 83…
## $ `11/6/20`      <dbl> 2257, 7184, 1092, 926, 2290, 663, 1045, 5146, 1483, 83…
## $ `11/7/20`      <dbl> 2286, 7226, 1095, 934, 2302, 664, 1051, 5179, 1485, 84…
## $ `11/8/20`      <dbl> 2307, 7263, 1098, 942, 2338, 664, 1053, 5214, 1488, 84…
## $ `11/9/20`      <dbl> 2328, 7345, 1107, 948, 2378, 665, 1061, 5246, 1506, 85…
## $ `11/10/20`     <dbl> 2328, 7348, 1107, 948, 2378, 665, 1061, 5254, 1507, 85…
## $ `11/11/20`     <dbl> 2351, 7409, 1112, 961, 2400, 668, 1062, 5282, 1508, 86…
## $ `11/12/20`     <dbl> 2385, 7454, 1113, 966, 2429, 669, 1062, 5345, 1514, 87…
## $ `11/13/20`     <dbl> 2417, 7523, 1117, 973, 2488, 673, 1068, 5429, 1545, 89…
## $ `11/14/20`     <dbl> 2435, 7596, 1123, 978, 2518, 675, 1075, 5470, 1556, 89…
## $ `11/15/20`     <dbl> 2456, 7646, 1128, 986, 2549, 677, 1087, 5608, 1570, 90…
## $ `11/16/20`     <dbl> 2481, 7696, 1130, 993, 2574, 677, 1095, 5666, 1572, 91…
## $ `11/17/20`     <dbl> 2506, 7772, 1134, 1004, 2594, 678, 1099, 5702, 1595, 9…
## $ `11/18/20`     <dbl> 2529, 7849, 1137, 1008, 2648, 678, 1102, 5764, 1620, 9…
## $ `11/19/20`     <dbl> 2554, 7933, 1145, 1011, 2683, 680, 1113, 5814, 1641, 9…
## $ `11/20/20`     <dbl> 2580, 8038, 1151, 1024, 2704, 684, 1120, 5896, 1663, 9…
## $ `11/21/20`     <dbl> 2597, 8131, 1157, 1036, 2735, 688, 1132, 5924, 1669, 9…
## $ `11/22/20`     <dbl> 2617, 8199, 1160, 1136, 2754, 689, 1133, 5964, 1675, 9…
## $ `11/23/20`     <dbl> 2634, 8269, 1161, 1142, 2763, 690, 1137, 5997, 1680, 9…
## $ `11/24/20`     <dbl> 2661, 8376, 1167, 1157, 2822, 690, 1143, 6049, 1714, 9…
## $ `11/25/20`     <dbl> 2686, 8473, 1170, 1162, 2855, 691, 1144, 6112, 1737, 1…
## $ `11/26/20`     <dbl> 2704, 8576, 1170, 1170, 2879, 694, 1153, 6215, 1764, 1…
## $ `11/27/20`     <dbl> 2716, 8603, 1171, 1173, 2888, 694, 1153, 6240, 1765, 1…
## $ `11/28/20`     <dbl> 2735, 8733, 1173, 1179, 2922, 696, 1165, 6301, 1768, 1…
## $ `11/29/20`     <dbl> 2751, 8820, 1175, 1188, 2946, 700, 1173, 6366, 1772, 1…
## $ `11/30/20`     <dbl> 2780, 8890, 1178, 1196, 2997, 702, 1178, 6430, 1779, 1…
## $ `12/1/20`      <dbl> 2818, 9051, 1189, 1204, 3061, 701, 1186, 6598, 1827, 1…
## $ `12/2/20`      <dbl> 2873, 9163, 1206, 1239, 3100, 709, 1188, 6695, 1859, 1…
## $ `12/3/20`      <dbl> 2893, 9341, 1214, 1252, 3158, 709, 1200, 6809, 1875, 1…
## $ `12/4/20`      <dbl> 2945, 9501, 1217, 1270, 3231, 711, 1211, 6939, 1891, 1…
## $ `12/5/20`      <dbl> 2979, 9626, 1219, 1283, 3281, 713, 1225, 7027, 1901, 1…
## $ `12/6/20`      <dbl> 3005, 9728, 1223, 1293, 3299, 713, 1236, 7096, 1906, 1…
## $ `12/7/20`      <dbl> 3043, 9821, 1224, 1299, 3324, 714, 1244, 7165, 1915, 1…
## $ `12/8/20`      <dbl> 3087, 9974, 1240, 1317, 3426, 719, 1257, 7300, 1945, 1…
## $ `12/9/20`      <dbl> 3117, 10087, 1245, 1322, 3496, 722, 1263, 7392, 1961, …
## $ `12/10/20`     <dbl> 3186, 10288, 1258, 1359, 3600, 722, 1287, 7534, 1977, …
## $ `12/11/20`     <dbl> 3233, 10489, 1264, 1398, 3663, 723, 1289, 7658, 1982, …
## $ `12/12/20`     <dbl> 3258, 10665, 1269, 1417, 3744, 725, 1306, 7760, 1997, …
## $ `12/13/20`     <dbl> 3300, 10806, 1272, 1441, 3776, 728, 1330, 7813, 2013, …
## $ `12/14/20`     <dbl> 3329, 10898, 1275, 1455, 3803, 728, 1340, 7872, 2022, …
## $ `12/15/20`     <dbl> 3426, 11061, 1292, 1504, 3881, 733, 1332, 7966, 2040, …
## $ `12/16/20`     <dbl> 3510, 11212, 1296, 1520, 3950, 737, 1343, 8072, 2064, …
## $ `12/17/20`     <dbl> 3570, 11364, 1309, 1548, 4036, 742, 1368, 8290, 2076, …
## $ `12/18/20`     <dbl> 3647, 11556, 1318, 1577, 4118, 747, 1384, 8459, 2090, …
## $ `12/19/20`     <dbl> 3698, 11722, 1330, 1601, 4191, 752, 1393, 8594, 2116, …
## $ `12/20/20`     <dbl> 3741, 11827, 1336, 1613, 4218, 753, 1399, 8648, 2125, …
## $ `12/21/20`     <dbl> 3780, 11952, 1336, 1628, 4234, 754, 1405, 8684, 2133, …
## $ `12/22/20`     <dbl> 3841, 12155, 1363, 1660, 4313, 760, 1412, 8856, 2161, …
## $ `12/23/20`     <dbl> 3889, 12321, 1383, 1683, 4367, 765, 1423, 8968, 2176, …
## $ `12/24/20`     <dbl> 3942, 12521, 1390, 1711, 4405, 770, 1434, 9071, 2191, …
## $ `12/25/20`     <dbl> 3990, 12666, 1396, 1725, 4441, 777, 1448, 9167, 2200, …
## $ `12/26/20`     <dbl> 3999, 12708, 1398, 1739, 4446, 825, 1446, 9198, 2203, …
## $ `12/27/20`     <dbl> 4029, 12825, 1406, 1746, 4465, 827, 1452, 9232, 2214, …
## $ `12/28/20`     <dbl> 4065, 12962, 1417, 1762, 4483, 830, 1457, 9286, 2229, …
## $ `12/29/20`     <dbl> 4105, 13172, 1462, 1792, 4535, 834, 1482, 9345, 2275, …
## $ `12/30/20`     <dbl> 4164, 13392, 1492, 1817, 4584, 846, 1493, 9428, 2310, …
## $ `12/31/20`     <dbl> 4190, 13601, 1514, 1834, 4641, 859, 1508, 9494, 2341, …
## $ `1/1/21`       <dbl> 4239, 13823, 1517, 1854, 4693, 888, 1522, 9584, 2366, …
## $ `1/2/21`       <dbl> 4268, 13955, 1528, 1863, 4729, 892, 1530, 9692, 2386, …
## $ `1/3/21`       <dbl> 4305, 14064, 1530, 1882, 4746, 900, 1546, 9731, 2402, …
## $ `1/4/21`       <dbl> 4336, 14187, 1533, 1885, 4771, 910, 1554, 9752, 2415, …
## $ `1/5/21`       <dbl> 4546, 14440, 1575, 1923, 4849, 920, 1574, 9975, 2474, …
## $ `1/6/21`       <dbl> 4645, 14656, 1597, 1944, 4898, 925, 1583, 10109, 2519,…
## $ `1/7/21`       <dbl> 4705, 14845, 1614, 1981, 4957, 927, 1598, 10283, 2552,…
## $ `1/8/21`       <dbl> 4770, 15052, 1634, 2015, 5018, 949, 1610, 10372, 2592,…
## $ `1/9/21`       <dbl> 4847, 15202, 1648, 2038, 5047, 950, 1625, 10453, 2620,…
## $ `1/10/21`      <dbl> 4879, 15327, 1658, 2051, 5066, 953, 1632, 10497, 2639,…
## $ `1/11/21`      <dbl> 4902, 15417, 1663, 2060, 5080, 957, 1637, 10537, 2651,…
## $ `1/12/21`      <dbl> 4970, 15572, 1679, 2090, 5134, 967, 1649, 10668, 2697,…
## $ `1/13/21`      <dbl> 4998, 15701, 1685, 2109, 5170, 966, 1651, 10745, 2734,…
## $ `1/14/21`      <dbl> 5075, 15841, 1696, 2113, 5219, 971, 1669, 10863, 2757,…
## $ `1/15/21`      <dbl> 5103, 16002, 1712, 2130, 5264, 981, 1679, 10982, 2778,…
## $ `1/16/21`      <dbl> 5154, 16176, 1723, 2144, 5292, 987, 1684, 11078, 2818,…
## $ `1/17/21`      <dbl> 5184, 16251, 1729, 2151, 5304, 990, 1696, 11122, 2827,…
## $ `1/18/21`      <dbl> 5198, 16346, 1730, 2162, 5308, 991, 1702, 11161, 2842,…
## $ `1/19/21`      <dbl> 5227, 16513, 1738, 2170, 5320, 997, 1707, 11206, 2886,…
## $ `1/20/21`      <dbl> 5257, 16653, 1760, 2188, 5376, 1011, 1708, 11292, 2931…
## $ `1/21/21`      <dbl> 5270, 16798, 1778, 2198, 5411, 1014, 1713, 11365, 2973…
## $ `1/22/21`      <dbl> 5327, 16981, 1793, 2212, 5439, 1022, 1724, 11441, 3011…
## $ `1/23/21`      <dbl> 5358, 17128, 1805, 2223, 5462, 1033, 1731, 11496, 3034…
## $ `1/24/21`      <dbl> 5376, 17256, 1827, 2223, 5473, 1035, 1744, 11521, 3042…
## $ `1/25/21`      <dbl> 5407, 17333, 1834, 2229, 5485, 1046, 1748, 11555, 3054…
## $ `1/26/21`      <dbl> 5440, 17496, 1882, 2247, 5517, 1058, 1759, 11626, 3085…
## $ `1/27/21`      <dbl> 5499, 17629, 1898, 2261, 5568, 1074, 1766, 11730, 3137…
## $ `1/28/21`      <dbl> 5554, 17779, 1920, 2271, 5612, 1079, 1788, 11833, 3159…
## $ `1/29/21`      <dbl> 5596, 17922, 1931, 2284, 5655, 1075, 1800, 11918, 3174…
## $ `1/30/21`      <dbl> 5596, 17922, 1931, 2284, 5655, 1075, 1800, 11918, 3174…
## $ `1/31/21`      <dbl> 5669, 18126, 1951, 2307, 5713, 1086, 1812, 12011, 3203…
## $ `2/1/21`       <dbl> 5683, 18211, 1956, 2309, 5720, 1089, 1827, 12062, 3210…
## $ `2/2/21`       <dbl> 5723, 18344, 1966, 2319, 5745, 1087, 1833, 12102, 3219…
## $ `2/3/21`       <dbl> 5753, 18418, 1981, 2321, 5768, 1093, 1838, 12179, 3233…
## $ `2/4/21`       <dbl> 5811, 18494, 1989, 2327, 5842, 1107, 1847, 12253, 3239…
## $ `2/5/21`       <dbl> 5824, 18568, 1994, 2331, 5871, 1113, 1853, 12325, 3249…
## $ `2/6/21`       <dbl> 5856, 18668, 2002, 2334, 5908, 1121, 1863, 12368, 3259…
## $ `2/7/21`       <dbl> 5869, 18723, 2008, 2339, 5915, 1128, 1865, 12402, 3263…
## $ `2/8/21`       <dbl> 5881, 18763, 2008, 2346, 5920, 1132, 1868, 12426, 3266…
## $ `2/9/21`       <dbl> 5910, 18824, 2019, 2362, 5929, 1132, 1872, 12477, 3283…
## $ `2/10/21`      <dbl> 5930, 18888, 2024, 2368, 5937, 1131, 1882, 12498, 3291…
## $ `2/11/21`      <dbl> 5970, 18960, 2030, 2377, 5955, 1136, 1886, 12539, 3305…
## $ `2/12/21`      <dbl> 5984, 18994, 2036, 2385, 5953, 1137, 1892, 12577, 3313…
## $ `2/13/21`      <dbl> 6002, 19051, 2040, 2393, 5957, 1139, 1898, 12629, 3318…
## $ `2/14/21`      <dbl> 6023, 19105, 2042, 2395, 5961, 1142, 1902, 12700, 3321…
## $ `2/15/21`      <dbl> 6024, 19136, 2044, 2397, 5973, 1142, 1905, 12725, 3325…
## $ `2/16/21`      <dbl> 6038, 19176, 2055, 2400, 5987, 1145, 1910, 12756, 3336…
## $ `2/17/21`      <dbl> 6050, 19267, 2053, 2399, 5997, 1143, 1924, 12784, 3338…
## $ `2/18/21`      <dbl> 6071, 19324, 2057, 2405, 6008, 1144, 1930, 12833, 3348…
## $ `2/19/21`      <dbl> 6079, 19361, 2061, 2411, 6021, 1147, 1934, 12860, 3358…
## $ `2/20/21`      <dbl> 6092, 19392, 2067, 2414, 6040, 1149, 1938, 12915, 3364…
## $ `2/21/21`      <dbl> 6117, 19433, 2070, 2416, 6042, 1151, 1940, 12940, 3367…
## $ `2/22/21`      <dbl> 6121, 19461, 2074, 2417, 6043, 1153, 1945, 13017, 3367…
## $ `2/23/21`      <dbl> 6143, 19554, 2084, 2432, 6058, 1160, 1948, 13063, 3382…

Import problems

problems(c19_raw)

Reshape and process the data to support an analysis and visualization

In this section of the analysis we are going to extract a useful subset of columns from our three datasets and generate derived datasets that can be used for further analysis and visualization. In support of this activity we are going to illustrate the use of several tidyverse dplyr commands:

Extract some key columns from the ACS dataset

The columns that we are interested in for this example are all of the populations for each age category. We are also extracting the full indentifier column (id), a subset of the identifier column that represents the combined FIPS code for the state and county (five characters, st_county), and a descriptive name of the geography (area_name). In the process of extracting the columns the long default names are replaced with more managable ones.

acs5_working <- acs5_raw %>% 
  mutate(
    st_county = str_sub(id, -5)
  ) %>% 
  select(
    id = id,
    st_county,
    area_name = `Geographic Area Name`,
    pop_total = `Estimate!!Total!!Total population`,
    pop_lt5 =   `Estimate!!Total!!Total population!!AGE!!Under 5 years`,
    pop_5_9 =   `Estimate!!Total!!Total population!!AGE!!5 to 9 years`,
    pop_10_14 = `Estimate!!Total!!Total population!!AGE!!10 to 14 years`,
    pop_15_19 = `Estimate!!Total!!Total population!!AGE!!15 to 19 years`,
    pop_20_24 = `Estimate!!Total!!Total population!!AGE!!20 to 24 years`,
    pop_25_29 = `Estimate!!Total!!Total population!!AGE!!25 to 29 years`,
    pop_30_34 = `Estimate!!Total!!Total population!!AGE!!30 to 34 years`,
    pop_35_39 = `Estimate!!Total!!Total population!!AGE!!35 to 39 years`,
    pop_40_44 = `Estimate!!Total!!Total population!!AGE!!40 to 44 years`,
    pop_45_49 = `Estimate!!Total!!Total population!!AGE!!45 to 49 years`,
    pop_50_54 = `Estimate!!Total!!Total population!!AGE!!50 to 54 years`,
    pop_55_59 = `Estimate!!Total!!Total population!!AGE!!55 to 59 years`,
    pop_60_64 = `Estimate!!Total!!Total population!!AGE!!60 to 64 years`,
    pop_65_69 = `Estimate!!Total!!Total population!!AGE!!65 to 69 years`,
    pop_70_74 = `Estimate!!Total!!Total population!!AGE!!70 to 74 years`,
    pop_75_79 = `Estimate!!Total!!Total population!!AGE!!75 to 79 years`,
    pop_80_84 = `Estimate!!Total!!Total population!!AGE!!80 to 84 years`,
    pop_gt84 =  `Estimate!!Total!!Total population!!AGE!!85 years and over`
  ) %>% 
  mutate(
    pop_lt20 = pop_lt5 + pop_5_9 + pop_10_14 + pop_15_19,
    pop_gte65 = pop_65_69 + pop_70_74 + pop_75_79 + pop_80_84 + pop_gt84,
    pct_lt20 = pop_lt20/pop_total,
    pct_gte65 = pop_gte65/pop_total
  )
glimpse(acs5_working)
## Observations: 3,220
## Variables: 26
## $ id        <chr> "0500000US01001", "0500000US01003", "0500000US01005", "0500…
## $ st_county <chr> "01001", "01003", "01005", "01007", "01009", "01011", "0101…
## $ area_name <chr> "Autauga County, Alabama", "Baldwin County, Alabama", "Barb…
## $ pop_total <dbl> 55200, 208107, 25782, 22527, 57645, 10352, 20025, 115098, 3…
## $ pop_lt5   <dbl> 3263, 11609, 1390, 1275, 3485, 596, 1205, 6562, 1950, 1204,…
## $ pop_5_9   <dbl> 4009, 11689, 1450, 1178, 3632, 634, 1293, 6844, 1728, 1470,…
## $ pop_10_14 <dbl> 3570, 14323, 1677, 1289, 3995, 540, 1274, 7158, 2099, 1520,…
## $ pop_15_19 <dbl> 3855, 12707, 1434, 1514, 3717, 772, 1292, 7773, 1960, 1603,…
## $ pop_20_24 <dbl> 3337, 10790, 1658, 1491, 3189, 706, 1073, 7626, 2525, 1383,…
## $ pop_25_29 <dbl> 3660, 11825, 1863, 1557, 3400, 702, 1238, 8017, 1829, 1437,…
## $ pop_30_34 <dbl> 3404, 11501, 1812, 1518, 3386, 418, 1186, 7000, 1862, 1092,…
## $ pop_35_39 <dbl> 4095, 12428, 1656, 1455, 3040, 862, 1213, 7039, 1861, 1561,…
## $ pop_40_44 <dbl> 3279, 12949, 1448, 1440, 4113, 737, 1188, 6806, 2112, 1356,…
## $ pop_45_49 <dbl> 3874, 13694, 1672, 1813, 3959, 611, 1168, 7148, 2281, 1749,…
## $ pop_50_54 <dbl> 3979, 14636, 1780, 1626, 3988, 711, 1259, 7810, 2367, 1893,…
## $ pop_55_59 <dbl> 4131, 14440, 1657, 1494, 3895, 593, 1460, 7851, 2638, 2047,…
## $ pop_60_64 <dbl> 2694, 14851, 1651, 1216, 3613, 854, 1370, 8078, 2205, 1917,…
## $ pop_65_69 <dbl> 2271, 13141, 1515, 1280, 3330, 575, 1087, 6667, 2089, 1934,…
## $ pop_70_74 <dbl> 2440, 11410, 1305, 842, 2802, 436, 994, 4822, 1686, 1492, 2…
## $ pop_75_79 <dbl> 1498, 7373, 841, 624, 1776, 248, 753, 3524, 1185, 1188, 138…
## $ pop_80_84 <dbl> 1026, 4792, 551, 488, 1459, 182, 415, 2323, 747, 528, 804, …
## $ pop_gt84  <dbl> 815, 3949, 422, 427, 866, 175, 557, 2050, 702, 479, 602, 29…
## $ pop_lt20  <dbl> 14697, 50328, 5951, 5256, 14829, 2542, 5064, 28337, 7737, 5…
## $ pop_gte65 <dbl> 8050, 40665, 4634, 3661, 10233, 1616, 3806, 19386, 6409, 56…
## $ pct_lt20  <dbl> 0.2662, 0.2418, 0.2308, 0.2333, 0.2572, 0.2456, 0.2529, 0.2…
## $ pct_gte65 <dbl> 0.1458, 0.1954, 0.1797, 0.1625, 0.1775, 0.1561, 0.1901, 0.1…

Extract key columns from LAD dataset

The coluns to be extracted from this dataset include the FIPS code for the state and county (STCOU in the original dataset, renamed to st_county), the descriptive area name (Areaname in the original dataset, renamed to area_name), and land area in sq. miles from the 2010 census data set (LND110210D in the original dataset, renamce to land_area_sqmi).

lad_working <- lad_raw %>% 
  select(
    st_county = STCOU,
    area_name = Areaname,
    land_area_sqmi = LND110210D
  )
glimpse(lad_working)
## Observations: 3,198
## Variables: 3
## $ st_county      <chr> "00000", "01000", "01001", "01003", "01005", "01007", …
## $ area_name      <chr> "UNITED STATES", "ALABAMA", "Autauga, AL", "Baldwin, A…
## $ land_area_sqmi <dbl> 3531905.4, 50645.3, 594.4, 1589.8, 884.9, 622.6, 644.8…

Build a reference table of state FIPS codes for later use in analysis and visualization

state_fips <- lad_raw %>% 
  filter(str_sub(STCOU, -3) == "000") %>% 
  mutate(
    st_fips = str_sub(STCOU, 1, 2)
  ) %>% 
  select(
    st_fips,
    Areaname
  )
glimpse(state_fips)
## Observations: 52
## Variables: 2
## $ st_fips  <chr> "00", "01", "02", "04", "05", "06", "08", "09", "10", "11", …
## $ Areaname <chr> "UNITED STATES", "ALABAMA", "ALASKA", "ARIZONA", "ARKANSAS",…

Extract and reshape the C19 dataset

First generate the geography identifier that matches the other datasets from the UID field - st_county. Then extract the descriptive name for the geography Combined_Key, and thre remaining date columns for which there are associated confirmed infection counts.

c19_working_wide <- c19_raw %>% 
  mutate(
    st_county = str_sub(UID, -5)
  ) %>% 
  select(
    -c(
      iso2, 
      iso3, 
      code3, 
      FIPS, 
      Admin2, 
      Province_State, 
      Country_Region, 
      Lat, 
      Long_
      ),
    st_county
  )
glimpse(c19_working_wide)
## Observations: 3,340
## Variables: 402
## $ UID          <dbl> 84001001, 84001003, 84001005, 84001007, 84001009, 840010…
## $ Combined_Key <chr> "Autauga, Alabama, US", "Baldwin, Alabama, US", "Barbour…
## $ `1/22/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/23/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/24/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/25/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/26/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/27/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/28/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/29/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/30/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `1/31/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/1/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/2/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/3/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/4/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/5/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/6/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/7/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/8/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/9/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/10/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/11/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/12/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/13/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/14/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/15/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/16/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/17/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/18/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/19/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/20/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/21/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/22/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/23/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/24/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/25/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/26/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/27/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/28/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `2/29/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/1/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/2/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/3/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/4/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/5/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/6/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/7/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/8/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/9/20`     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/10/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/11/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/12/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/13/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/14/20`    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/15/20`    <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/16/20`    <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/17/20`    <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/18/20`    <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/19/20`    <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/20/20`    <dbl> 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/21/20`    <dbl> 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/22/20`    <dbl> 0, 2, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/23/20`    <dbl> 0, 3, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/24/20`    <dbl> 1, 5, 0, 0, 0, 0, 0, 2, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3/25/20`    <dbl> 5, 5, 0, 0, 3, 0, 1, 2, 9, 1, 4, 0, 0, 1, 1, 0, 1, 0, 1,…
## $ `3/26/20`    <dbl> 6, 6, 0, 0, 4, 1, 1, 2, 13, 1, 4, 1, 0, 1, 1, 0, 1, 0, 2…
## $ `3/27/20`    <dbl> 6, 6, 0, 2, 6, 1, 1, 4, 15, 1, 6, 1, 0, 1, 3, 0, 1, 0, 2…
## $ `3/28/20`    <dbl> 6, 18, 0, 2, 6, 2, 1, 4, 22, 1, 7, 1, 0, 2, 4, 0, 2, 0, …
## $ `3/29/20`    <dbl> 6, 19, 0, 2, 7, 2, 1, 4, 29, 1, 8, 1, 0, 2, 5, 0, 2, 0, …
## $ `3/30/20`    <dbl> 8, 22, 0, 3, 7, 2, 1, 9, 39, 1, 10, 2, 0, 2, 5, 0, 4, 0,…
## $ `3/31/20`    <dbl> 8, 23, 0, 3, 7, 2, 1, 10, 40, 1, 11, 4, 0, 2, 5, 0, 4, 0…
## $ `4/1/20`     <dbl> 10, 26, 0, 3, 7, 2, 1, 11, 51, 1, 13, 5, 2, 3, 6, 1, 4, …
## $ `4/2/20`     <dbl> 12, 29, 0, 4, 9, 2, 1, 12, 75, 3, 14, 5, 2, 7, 6, 4, 5, …
## $ `4/3/20`     <dbl> 12, 31, 2, 4, 11, 2, 1, 22, 87, 4, 15, 5, 3, 8, 7, 7, 5,…
## $ `4/4/20`     <dbl> 12, 34, 3, 4, 12, 2, 1, 23, 91, 5, 15, 5, 7, 9, 7, 8, 6,…
## $ `4/5/20`     <dbl> 12, 39, 3, 7, 13, 2, 1, 26, 94, 5, 18, 6, 9, 9, 7, 8, 7,…
## $ `4/6/20`     <dbl> 12, 43, 4, 7, 13, 2, 1, 40, 102, 5, 20, 8, 9, 9, 9, 9, 8…
## $ `4/7/20`     <dbl> 12, 47, 4, 8, 13, 2, 2, 51, 112, 5, 21, 8, 10, 9, 12, 9,…
## $ `4/8/20`     <dbl> 12, 54, 4, 9, 13, 3, 3, 54, 145, 7, 23, 8, 10, 11, 12, 1…
## $ `4/9/20`     <dbl> 17, 63, 8, 11, 15, 4, 3, 56, 170, 7, 26, 8, 13, 11, 12, …
## $ `4/10/20`    <dbl> 18, 64, 9, 11, 15, 4, 3, 56, 178, 7, 29, 10, 13, 11, 12,…
## $ `4/11/20`    <dbl> 19, 70, 10, 13, 15, 4, 6, 59, 189, 7, 31, 11, 15, 12, 12…
## $ `4/12/20`    <dbl> 19, 75, 10, 16, 17, 4, 7, 63, 206, 9, 31, 12, 18, 14, 12…
## $ `4/13/20`    <dbl> 19, 82, 10, 17, 18, 6, 8, 64, 215, 9, 35, 12, 18, 14, 12…
## $ `4/14/20`    <dbl> 23, 93, 11, 18, 19, 8, 8, 65, 219, 9, 35, 14, 18, 14, 12…
## $ `4/15/20`    <dbl> 24, 100, 13, 19, 21, 8, 11, 66, 229, 10, 38, 15, 19, 14,…
## $ `4/16/20`    <dbl> 24, 104, 14, 23, 22, 8, 11, 68, 234, 11, 38, 15, 21, 14,…
## $ `4/17/20`    <dbl> 24, 106, 15, 23, 22, 8, 13, 68, 239, 11, 39, 15, 21, 14,…
## $ `4/18/20`    <dbl> 25, 110, 18, 26, 24, 9, 14, 71, 242, 12, 41, 16, 21, 16,…
## $ `4/19/20`    <dbl> 26, 116, 20, 26, 25, 9, 15, 78, 247, 12, 45, 16, 21, 17,…
## $ `4/20/20`    <dbl> 28, 120, 22, 30, 27, 11, 15, 82, 257, 13, 45, 18, 22, 18…
## $ `4/21/20`    <dbl> 30, 126, 28, 30, 31, 11, 15, 85, 259, 14, 45, 18, 22, 18…
## $ `4/22/20`    <dbl> 32, 134, 29, 32, 33, 11, 17, 87, 266, 14, 47, 21, 22, 18…
## $ `4/23/20`    <dbl> 33, 143, 31, 32, 35, 12, 19, 89, 273, 14, 49, 23, 23, 18…
## $ `4/24/20`    <dbl> 36, 148, 33, 33, 36, 12, 21, 89, 277, 14, 51, 23, 23, 18…
## $ `4/25/20`    <dbl> 36, 155, 34, 34, 36, 12, 28, 90, 279, 14, 52, 25, 24, 18…
## $ `4/26/20`    <dbl> 36, 161, 34, 37, 38, 12, 32, 91, 281, 15, 53, 30, 25, 18…
## $ `4/27/20`    <dbl> 37, 167, 35, 40, 38, 12, 34, 91, 284, 15, 53, 36, 26, 18…
## $ `4/28/20`    <dbl> 39, 172, 38, 40, 38, 12, 48, 94, 286, 16, 54, 37, 27, 18…
## $ `4/29/20`    <dbl> 41, 174, 38, 40, 39, 12, 53, 95, 288, 16, 54, 37, 30, 20…
## $ `4/30/20`    <dbl> 42, 180, 38, 40, 39, 13, 55, 97, 292, 16, 55, 44, 31, 20…
## $ `5/1/20`     <dbl> 43, 180, 42, 41, 41, 14, 68, 97, 295, 16, 55, 50, 33, 20…
## $ `5/2/20`     <dbl> 47, 185, 43, 41, 42, 14, 93, 102, 296, 16, 56, 50, 37, 2…
## $ `5/3/20`     <dbl> 51, 190, 45, 42, 42, 14, 104, 109, 302, 19, 59, 50, 41, …
## $ `5/4/20`     <dbl> 54, 190, 45, 43, 42, 15, 113, 109, 307, 19, 61, 54, 41, …
## $ `5/5/20`     <dbl> 54, 194, 47, 43, 42, 17, 118, 118, 309, 19, 64, 56, 42, …
## $ `5/6/20`     <dbl> 56, 199, 47, 43, 44, 17, 128, 120, 313, 20, 64, 56, 44, …
## $ `5/7/20`     <dbl> 58, 207, 50, 44, 46, 17, 152, 126, 317, 20, 69, 57, 48, …
## $ `5/8/20`     <dbl> 62, 211, 52, 44, 47, 20, 159, 129, 320, 22, 69, 60, 51, …
## $ `5/9/20`     <dbl> 63, 219, 57, 45, 47, 21, 175, 130, 323, 23, 70, 62, 51, …
## $ `5/10/20`    <dbl> 72, 224, 59, 46, 47, 22, 188, 130, 325, 23, 72, 67, 55, …
## $ `5/11/20`    <dbl> 81, 226, 61, 46, 48, 26, 196, 131, 327, 24, 74, 68, 62, …
## $ `5/12/20`    <dbl> 88, 230, 67, 46, 48, 26, 223, 132, 332, 24, 76, 70, 64, …
## $ `5/13/20`    <dbl> 90, 237, 70, 46, 48, 27, 231, 133, 333, 24, 76, 73, 65, …
## $ `5/14/20`    <dbl> 100, 247, 74, 47, 48, 27, 244, 134, 334, 24, 78, 77, 67,…
## $ `5/15/20`    <dbl> 100, 253, 79, 51, 48, 32, 254, 135, 335, 25, 79, 81, 70,…
## $ `5/16/20`    <dbl> 108, 261, 81, 52, 49, 35, 265, 136, 337, 26, 81, 84, 74,…
## $ `5/17/20`    <dbl> 118, 267, 85, 52, 49, 39, 278, 139, 337, 27, 83, 85, 75,…
## $ `5/18/20`    <dbl> 124, 267, 89, 53, 50, 50, 289, 139, 338, 28, 84, 89, 77,…
## $ `5/19/20`    <dbl> 130, 269, 92, 53, 50, 58, 297, 142, 339, 30, 85, 124, 86…
## $ `5/20/20`    <dbl> 135, 277, 96, 54, 50, 64, 306, 142, 342, 30, 87, 127, 89…
## $ `5/21/20`    <dbl> 148, 277, 101, 54, 51, 71, 315, 143, 343, 31, 88, 132, 9…
## $ `5/22/20`    <dbl> 151, 278, 106, 56, 52, 95, 325, 145, 343, 32, 89, 135, 9…
## $ `5/23/20`    <dbl> 156, 280, 108, 59, 52, 103, 329, 145, 343, 32, 90, 139, …
## $ `5/24/20`    <dbl> 160, 281, 113, 60, 52, 109, 338, 148, 349, 32, 91, 140, …
## $ `5/25/20`    <dbl> 171, 284, 116, 63, 52, 138, 360, 153, 350, 33, 91, 143, …
## $ `5/26/20`    <dbl> 191, 285, 128, 68, 54, 167, 375, 157, 353, 33, 95, 146, …
## $ `5/27/20`    <dbl> 192, 289, 133, 72, 56, 175, 387, 159, 356, 33, 99, 146, …
## $ `5/28/20`    <dbl> 204, 289, 136, 72, 61, 185, 389, 159, 364, 34, 102, 147,…
## $ `5/29/20`    <dbl> 211, 290, 151, 72, 63, 201, 394, 160, 367, 36, 104, 148,…
## $ `5/30/20`    <dbl> 216, 292, 155, 74, 65, 205, 403, 161, 369, 37, 104, 150,…
## $ `5/31/20`    <dbl> 227, 298, 172, 77, 66, 209, 413, 169, 370, 38, 105, 152,…
## $ `6/1/20`     <dbl> 237, 299, 177, 78, 66, 211, 414, 172, 373, 38, 106, 152,…
## $ `6/2/20`     <dbl> 239, 301, 181, 78, 66, 215, 418, 172, 374, 38, 108, 152,…
## $ `6/3/20`     <dbl> 241, 302, 181, 78, 66, 217, 420, 174, 375, 38, 111, 152,…
## $ `6/4/20`     <dbl> 248, 305, 187, 78, 67, 219, 430, 176, 379, 38, 112, 152,…
## $ `6/5/20`     <dbl> 259, 312, 194, 79, 73, 225, 441, 181, 389, 40, 112, 154,…
## $ `6/6/20`     <dbl> 264, 321, 196, 79, 76, 231, 447, 183, 393, 42, 115, 154,…
## $ `6/7/20`     <dbl> 271, 327, 200, 81, 78, 237, 453, 184, 398, 42, 115, 155,…
## $ `6/8/20`     <dbl> 282, 332, 201, 87, 80, 242, 462, 186, 407, 42, 121, 156,…
## $ `6/9/20`     <dbl> 295, 338, 210, 91, 84, 247, 469, 188, 418, 43, 124, 157,…
## $ `6/10/20`    <dbl> 315, 350, 215, 95, 92, 252, 482, 190, 434, 43, 127, 161,…
## $ `6/11/20`    <dbl> 323, 357, 220, 97, 98, 256, 498, 193, 443, 46, 128, 163,…
## $ `6/12/20`    <dbl> 334, 365, 226, 102, 106, 275, 517, 197, 457, 48, 134, 16…
## $ `6/13/20`    <dbl> 361, 367, 233, 106, 114, 300, 534, 201, 474, 52, 138, 17…
## $ `6/14/20`    <dbl> 369, 375, 237, 113, 120, 305, 542, 202, 486, 52, 143, 17…
## $ `6/15/20`    <dbl> 371, 377, 243, 113, 121, 306, 545, 203, 488, 52, 143, 17…
## $ `6/16/20`    <dbl> 377, 379, 252, 116, 125, 310, 548, 206, 491, 53, 151, 17…
## $ `6/17/20`    <dbl> 404, 389, 264, 119, 135, 316, 560, 208, 503, 54, 154, 18…
## $ `6/18/20`    <dbl> 415, 402, 267, 125, 140, 317, 562, 208, 507, 57, 157, 18…
## $ `6/19/20`    <dbl> 435, 408, 272, 125, 144, 324, 563, 209, 515, 57, 162, 18…
## $ `6/20/20`    <dbl> 438, 418, 272, 126, 151, 324, 567, 211, 520, 57, 165, 18…
## $ `6/21/20`    <dbl> 447, 426, 277, 128, 155, 325, 569, 213, 527, 57, 170, 18…
## $ `6/22/20`    <dbl> 458, 439, 280, 134, 162, 327, 572, 214, 533, 57, 176, 18…
## $ `6/23/20`    <dbl> 474, 453, 288, 141, 170, 330, 574, 223, 540, 58, 184, 18…
## $ `6/24/20`    <dbl> 480, 466, 305, 148, 177, 339, 580, 233, 547, 62, 184, 18…
## $ `6/25/20`    <dbl> 493, 505, 313, 161, 186, 340, 586, 236, 558, 66, 192, 18…
## $ `6/26/20`    <dbl> 500, 549, 317, 164, 192, 346, 593, 245, 560, 67, 199, 18…
## $ `6/27/20`    <dbl> 505, 573, 317, 164, 192, 346, 595, 245, 560, 69, 200, 18…
## $ `6/28/20`    <dbl> 528, 628, 324, 167, 200, 354, 598, 261, 581, 72, 208, 19…
## $ `6/29/20`    <dbl> 538, 668, 327, 172, 209, 354, 599, 276, 590, 73, 211, 19…
## $ `6/30/20`    <dbl> 554, 691, 328, 176, 218, 356, 599, 278, 594, 76, 218, 19…
## $ `7/1/20`     <dbl> 562, 739, 337, 181, 221, 358, 602, 288, 609, 82, 221, 19…
## $ `7/2/20`     <dbl> 570, 831, 352, 191, 232, 363, 614, 330, 620, 89, 234, 19…
## $ `7/3/20`     <dbl> 584, 847, 354, 193, 236, 363, 615, 341, 631, 89, 244, 19…
## $ `7/4/20`     <dbl> 601, 861, 356, 196, 240, 363, 624, 363, 634, 101, 251, 1…
## $ `7/5/20`     <dbl> 620, 893, 358, 198, 249, 364, 624, 375, 643, 102, 258, 1…
## $ `7/6/20`     <dbl> 623, 979, 365, 202, 257, 365, 625, 390, 652, 106, 264, 1…
## $ `7/7/20`     <dbl> 656, 1042, 372, 204, 270, 366, 630, 412, 658, 115, 281, …
## $ `7/8/20`     <dbl> 663, 1118, 377, 214, 289, 366, 632, 443, 672, 115, 292, …
## $ `7/9/20`     <dbl> 670, 1175, 387, 219, 301, 372, 639, 460, 678, 116, 300, …
## $ `7/10/20`    <dbl> 691, 1215, 404, 226, 316, 373, 645, 496, 690, 126, 329, …
## $ `7/11/20`    <dbl> 708, 1280, 409, 229, 338, 373, 646, 520, 694, 128, 337, …
## $ `7/12/20`    <dbl> 736, 1349, 419, 232, 354, 374, 650, 562, 703, 135, 353, …
## $ `7/13/20`    <dbl> 749, 1396, 437, 237, 368, 376, 651, 580, 713, 139, 364, …
## $ `7/14/20`    <dbl> 764, 1506, 446, 243, 390, 377, 655, 649, 719, 144, 377, …
## $ `7/15/20`    <dbl> 785, 1588, 462, 248, 426, 380, 659, 663, 732, 149, 394, …
## $ `7/16/20`    <dbl> 797, 1675, 466, 255, 445, 383, 662, 706, 743, 155, 413, …
## $ `7/17/20`    <dbl> 822, 1806, 483, 262, 464, 389, 668, 728, 756, 160, 431, …
## $ `7/18/20`    <dbl> 850, 1925, 497, 270, 487, 390, 676, 772, 762, 171, 469, …
## $ `7/19/20`    <dbl> 862, 1996, 504, 277, 510, 392, 678, 813, 767, 177, 482, …
## $ `7/20/20`    <dbl> 872, 2085, 513, 283, 526, 393, 685, 849, 774, 181, 498, …
## $ `7/21/20`    <dbl> 885, 2169, 520, 288, 549, 396, 693, 870, 781, 183, 516, …
## $ `7/22/20`    <dbl> 905, 2344, 531, 290, 582, 401, 699, 893, 789, 192, 538, …
## $ `7/23/20`    <dbl> 918, 2482, 540, 304, 616, 405, 705, 974, 796, 205, 563, …
## $ `7/24/20`    <dbl> 939, 2610, 552, 318, 639, 410, 712, 1025, 810, 207, 588,…
## $ `7/25/20`    <dbl> 953, 2675, 563, 324, 654, 411, 718, 1086, 820, 209, 603,…
## $ `7/26/20`    <dbl> 971, 2733, 570, 335, 674, 412, 724, 1163, 824, 220, 622,…
## $ `7/27/20`    <dbl> 988, 2800, 576, 339, 679, 425, 728, 1206, 835, 221, 640,…
## $ `7/28/20`    <dbl> 995, 2873, 585, 345, 701, 427, 734, 1260, 844, 227, 664,…
## $ `7/29/20`    <dbl> 1006, 3001, 586, 353, 738, 431, 740, 1305, 848, 235, 677…
## $ `7/30/20`    <dbl> 1029, 3075, 598, 364, 773, 433, 746, 1412, 859, 238, 693…
## $ `7/31/20`    <dbl> 1042, 3116, 603, 369, 799, 439, 749, 1445, 862, 242, 709…
## $ `8/1/20`     <dbl> 1064, 3194, 611, 373, 820, 441, 756, 1541, 869, 254, 731…
## $ `8/2/20`     <dbl> 1078, 3241, 613, 383, 838, 442, 758, 1570, 876, 262, 743…
## $ `8/3/20`     <dbl> 1086, 3298, 615, 390, 841, 445, 758, 1608, 882, 268, 759…
## $ `8/4/20`     <dbl> 1086, 3347, 616, 393, 845, 448, 761, 1645, 887, 270, 775…
## $ `8/5/20`     <dbl> 1109, 3409, 621, 420, 883, 454, 763, 1704, 894, 281, 784…
## $ `8/6/20`     <dbl> 1126, 3473, 627, 424, 917, 458, 766, 1744, 900, 291, 809…
## $ `8/7/20`     <dbl> 1145, 3533, 629, 434, 929, 467, 766, 1795, 905, 298, 829…
## $ `8/8/20`     <dbl> 1175, 3575, 632, 446, 945, 468, 771, 1821, 906, 301, 834…
## $ `8/9/20`     <dbl> 1186, 3676, 633, 450, 957, 470, 774, 1847, 909, 302, 852…
## $ `8/10/20`    <dbl> 1200, 3700, 642, 454, 971, 486, 775, 1874, 915, 306, 862…
## $ `8/11/20`    <dbl> 1224, 3749, 648, 465, 979, 497, 778, 1902, 918, 311, 873…
## $ `8/12/20`    <dbl> 1229, 3783, 653, 469, 994, 497, 780, 1925, 919, 319, 877…
## $ `8/13/20`    <dbl> 1235, 3825, 658, 475, 998, 498, 786, 1941, 922, 326, 890…
## $ `8/14/20`    <dbl> 1245, 3881, 662, 479, 1004, 500, 796, 1980, 925, 333, 89…
## $ `8/15/20`    <dbl> 1252, 3923, 672, 481, 1014, 500, 799, 2000, 927, 337, 90…
## $ `8/16/20`    <dbl> 1258, 3936, 673, 485, 1023, 501, 803, 2015, 928, 341, 91…
## $ `8/17/20`    <dbl> 1276, 3959, 675, 487, 1060, 512, 803, 2061, 937, 350, 92…
## $ `8/18/20`    <dbl> 1281, 3994, 682, 501, 1086, 530, 805, 2121, 943, 351, 93…
## $ `8/19/20`    <dbl> 1293, 4029, 692, 507, 1113, 535, 809, 2181, 949, 358, 94…
## $ `8/20/20`    <dbl> 1304, 4058, 694, 512, 1133, 536, 811, 2211, 965, 362, 96…
## $ `8/21/20`    <dbl> 1316, 4100, 710, 515, 1147, 539, 813, 2246, 969, 367, 98…
## $ `8/22/20`    <dbl> 1318, 4132, 712, 521, 1149, 539, 815, 2260, 970, 369, 99…
## $ `8/23/20`    <dbl> 1337, 4148, 715, 521, 1160, 540, 816, 2285, 970, 374, 10…
## $ `8/24/20`    <dbl> 1343, 4171, 720, 522, 1177, 540, 817, 2302, 973, 376, 10…
## $ `8/25/20`    <dbl> 1357, 4247, 728, 526, 1224, 541, 824, 2360, 982, 379, 10…
## $ `8/26/20`    <dbl> 1365, 4296, 736, 530, 1239, 542, 827, 2380, 1012, 387, 1…
## $ `8/27/20`    <dbl> 1375, 4330, 741, 532, 1251, 544, 829, 2409, 1015, 390, 1…
## $ `8/28/20`    <dbl> 1391, 4408, 749, 539, 1273, 551, 845, 2468, 1023, 395, 1…
## $ `8/29/20`    <dbl> 1424, 4502, 752, 550, 1301, 555, 855, 2500, 1024, 402, 1…
## $ `8/30/20`    <dbl> 1429, 4519, 752, 553, 1312, 556, 857, 2530, 1029, 403, 1…
## $ `8/31/20`    <dbl> 1440, 4538, 759, 558, 1332, 564, 865, 2582, 1037, 414, 1…
## $ `9/1/20`     <dbl> 1442, 4563, 763, 562, 1336, 566, 867, 2606, 1040, 419, 1…
## $ `9/2/20`     <dbl> 1454, 4599, 765, 564, 1356, 567, 868, 2648, 1041, 426, 1…
## $ `9/3/20`     <dbl> 1462, 4626, 770, 567, 1376, 567, 872, 2714, 1047, 440, 1…
## $ `9/4/20`     <dbl> 1474, 4654, 770, 571, 1392, 567, 875, 2786, 1053, 447, 1…
## $ `9/5/20`     <dbl> 1477, 4684, 771, 576, 1399, 569, 879, 2797, 1055, 449, 1…
## $ `9/6/20`     <dbl> 1488, 4700, 772, 578, 1416, 569, 881, 2836, 1058, 452, 1…
## $ `9/7/20`     <dbl> 1494, 4725, 772, 585, 1418, 569, 881, 2840, 1058, 452, 1…
## $ `9/8/20`     <dbl> 1505, 4755, 772, 586, 1424, 569, 882, 2859, 1061, 455, 1…
## $ `9/9/20`     <dbl> 1526, 4796, 779, 589, 1441, 571, 883, 2900, 1069, 465, 1…
## $ `9/10/20`    <dbl> 1530, 4845, 781, 593, 1453, 571, 884, 2922, 1079, 471, 1…
## $ `9/11/20`    <dbl> 1543, 4881, 787, 594, 1459, 572, 886, 2975, 1085, 482, 1…
## $ `9/12/20`    <dbl> 1551, 4915, 789, 596, 1472, 578, 888, 3023, 1085, 502, 1…
## $ `9/13/20`    <dbl> 1567, 4934, 796, 599, 1483, 578, 889, 3040, 1087, 504, 1…
## $ `9/14/20`    <dbl> 1586, 4949, 801, 600, 1490, 579, 890, 3066, 1092, 506, 1…
## $ `9/15/20`    <dbl> 1601, 4964, 807, 601, 1504, 581, 892, 3106, 1097, 510, 1…
## $ `9/16/20`    <dbl> 1614, 4982, 807, 606, 1515, 583, 893, 3156, 1097, 513, 1…
## $ `9/17/20`    <dbl> 1650, 4994, 822, 607, 1538, 583, 896, 3189, 1109, 522, 1…
## $ `9/18/20`    <dbl> 1659, 5016, 825, 619, 1551, 584, 897, 3268, 1114, 539, 1…
## $ `9/19/20`    <dbl> 1675, 5029, 831, 623, 1564, 586, 897, 3289, 1121, 549, 1…
## $ `9/20/20`    <dbl> 1676, 5053, 833, 624, 1573, 590, 898, 3321, 1123, 552, 1…
## $ `9/21/20`    <dbl> 1697, 5090, 846, 628, 1586, 592, 898, 3341, 1131, 557, 1…
## $ `9/22/20`    <dbl> 1697, 5106, 848, 633, 1593, 596, 899, 3353, 1135, 567, 1…
## $ `9/23/20`    <dbl> 1711, 5127, 852, 637, 1605, 597, 901, 3412, 1142, 574, 1…
## $ `9/24/20`    <dbl> 1736, 5397, 868, 646, 1614, 598, 903, 3450, 1151, 586, 1…
## $ `9/25/20`    <dbl> 1750, 5419, 878, 649, 1619, 603, 906, 3466, 1154, 592, 1…
## $ `9/26/20`    <dbl> 1758, 5465, 882, 650, 1623, 605, 907, 3485, 1157, 596, 1…
## $ `9/27/20`    <dbl> 1770, 5524, 883, 651, 1624, 606, 908, 3498, 1161, 597, 1…
## $ `9/28/20`    <dbl> 1776, 5550, 883, 653, 1626, 606, 911, 3516, 1164, 601, 1…
## $ `9/29/20`    <dbl> 1785, 5592, 892, 660, 1633, 608, 913, 3530, 1170, 605, 1…
## $ `9/30/20`    <dbl> 1792, 5954, 894, 668, 1636, 611, 915, 3556, 1172, 609, 1…
## $ `10/1/20`    <dbl> 1799, 5981, 900, 671, 1644, 611, 917, 3597, 1175, 616, 1…
## $ `10/2/20`    <dbl> 1812, 6009, 917, 675, 1656, 611, 919, 3629, 1195, 625, 1…
## $ `10/3/20`    <dbl> 1821, 6034, 917, 683, 1659, 611, 920, 3647, 1196, 625, 1…
## $ `10/4/20`    <dbl> 1824, 6045, 917, 683, 1664, 611, 921, 3672, 1199, 628, 1…
## $ `10/5/20`    <dbl> 1832, 6075, 918, 688, 1667, 614, 923, 3700, 1201, 630, 1…
## $ `10/6/20`    <dbl> 1843, 6103, 923, 700, 1678, 616, 926, 3718, 1203, 636, 1…
## $ `10/7/20`    <dbl> 1847, 6114, 925, 703, 1686, 618, 928, 3736, 1212, 640, 1…
## $ `10/8/20`    <dbl> 1875, 6144, 937, 715, 1700, 622, 937, 3790, 1231, 648, 1…
## $ `10/9/20`    <dbl> 1894, 6164, 940, 724, 1712, 623, 948, 3811, 1235, 653, 1…
## $ `10/10/20`   <dbl> 1901, 6176, 940, 734, 1720, 624, 954, 3829, 1235, 657, 1…
## $ `10/11/20`   <dbl> 1907, 6192, 941, 736, 1732, 625, 958, 3848, 1238, 661, 1…
## $ `10/12/20`   <dbl> 1921, 6222, 948, 741, 1755, 626, 964, 3884, 1247, 674, 1…
## $ `10/13/20`   <dbl> 1925, 6247, 948, 742, 1765, 628, 974, 3911, 1251, 677, 1…
## $ `10/14/20`   <dbl> 1946, 6266, 963, 759, 1782, 628, 979, 3939, 1255, 687, 1…
## $ `10/15/20`   <dbl> 1958, 6313, 966, 769, 1796, 630, 985, 3992, 1258, 694, 1…
## $ `10/16/20`   <dbl> 1971, 6332, 975, 773, 1822, 633, 987, 4053, 1261, 705, 1…
## $ `10/17/20`   <dbl> 1985, 6350, 978, 783, 1837, 633, 993, 4071, 1261, 706, 1…
## $ `10/18/20`   <dbl> 1995, 6356, 978, 787, 1848, 634, 996, 4090, 1268, 707, 1…
## $ `10/19/20`   <dbl> 2006, 6384, 984, 789, 1863, 635, 996, 4115, 1298, 714, 1…
## $ `10/20/20`   <dbl> 2018, 6425, 993, 799, 1887, 636, 996, 4143, 1328, 717, 1…
## $ `10/21/20`   <dbl> 2021, 6459, 1007, 809, 1907, 636, 999, 4175, 1334, 720, …
## $ `10/22/20`   <dbl> 2027, 6599, 1010, 823, 1923, 638, 1000, 4213, 1341, 725,…
## $ `10/23/20`   <dbl> 2040, 6619, 1028, 825, 1934, 647, 1005, 4553, 1346, 727,…
## $ `10/24/20`   <dbl> 2055, 6642, 1030, 839, 1947, 648, 1009, 4587, 1347, 732,…
## $ `10/25/20`   <dbl> 2070, 6677, 1030, 841, 1958, 648, 1011, 4602, 1349, 736,…
## $ `10/26/20`   <dbl> 2079, 6694, 1038, 849, 1986, 649, 1011, 4634, 1365, 749,…
## $ `10/27/20`   <dbl> 2098, 6728, 1042, 858, 2002, 649, 1014, 4675, 1369, 756,…
## $ `10/28/20`   <dbl> 2120, 6757, 1052, 862, 2027, 650, 1018, 4757, 1379, 759,…
## $ `10/29/20`   <dbl> 2134, 6879, 1053, 867, 2054, 651, 1018, 4800, 1381, 764,…
## $ `10/30/20`   <dbl> 2154, 6931, 1058, 873, 2089, 651, 1021, 4855, 1389, 779,…
## $ `10/31/20`   <dbl> 2168, 6955, 1059, 877, 2109, 653, 1023, 4883, 1392, 782,…
## $ `11/1/20`    <dbl> 2182, 6974, 1062, 883, 2128, 653, 1025, 4913, 1397, 786,…
## $ `11/2/20`    <dbl> 2195, 6991, 1073, 890, 2178, 656, 1028, 4946, 1428, 804,…
## $ `11/3/20`    <dbl> 2210, 7054, 1077, 900, 2204, 658, 1032, 4996, 1449, 819,…
## $ `11/4/20`    <dbl> 2229, 7093, 1079, 907, 2233, 660, 1035, 5030, 1461, 830,…
## $ `11/5/20`    <dbl> 2244, 7133, 1089, 920, 2258, 662, 1043, 5072, 1469, 833,…
## $ `11/6/20`    <dbl> 2257, 7184, 1092, 926, 2290, 663, 1045, 5146, 1483, 837,…
## $ `11/7/20`    <dbl> 2286, 7226, 1095, 934, 2302, 664, 1051, 5179, 1485, 841,…
## $ `11/8/20`    <dbl> 2307, 7263, 1098, 942, 2338, 664, 1053, 5214, 1488, 846,…
## $ `11/9/20`    <dbl> 2328, 7345, 1107, 948, 2378, 665, 1061, 5246, 1506, 857,…
## $ `11/10/20`   <dbl> 2328, 7348, 1107, 948, 2378, 665, 1061, 5254, 1507, 857,…
## $ `11/11/20`   <dbl> 2351, 7409, 1112, 961, 2400, 668, 1062, 5282, 1508, 865,…
## $ `11/12/20`   <dbl> 2385, 7454, 1113, 966, 2429, 669, 1062, 5345, 1514, 870,…
## $ `11/13/20`   <dbl> 2417, 7523, 1117, 973, 2488, 673, 1068, 5429, 1545, 892,…
## $ `11/14/20`   <dbl> 2435, 7596, 1123, 978, 2518, 675, 1075, 5470, 1556, 898,…
## $ `11/15/20`   <dbl> 2456, 7646, 1128, 986, 2549, 677, 1087, 5608, 1570, 908,…
## $ `11/16/20`   <dbl> 2481, 7696, 1130, 993, 2574, 677, 1095, 5666, 1572, 912,…
## $ `11/17/20`   <dbl> 2506, 7772, 1134, 1004, 2594, 678, 1099, 5702, 1595, 919…
## $ `11/18/20`   <dbl> 2529, 7849, 1137, 1008, 2648, 678, 1102, 5764, 1620, 935…
## $ `11/19/20`   <dbl> 2554, 7933, 1145, 1011, 2683, 680, 1113, 5814, 1641, 956…
## $ `11/20/20`   <dbl> 2580, 8038, 1151, 1024, 2704, 684, 1120, 5896, 1663, 959…
## $ `11/21/20`   <dbl> 2597, 8131, 1157, 1036, 2735, 688, 1132, 5924, 1669, 979…
## $ `11/22/20`   <dbl> 2617, 8199, 1160, 1136, 2754, 689, 1133, 5964, 1675, 985…
## $ `11/23/20`   <dbl> 2634, 8269, 1161, 1142, 2763, 690, 1137, 5997, 1680, 989…
## $ `11/24/20`   <dbl> 2661, 8376, 1167, 1157, 2822, 690, 1143, 6049, 1714, 996…
## $ `11/25/20`   <dbl> 2686, 8473, 1170, 1162, 2855, 691, 1144, 6112, 1737, 100…
## $ `11/26/20`   <dbl> 2704, 8576, 1170, 1170, 2879, 694, 1153, 6215, 1764, 101…
## $ `11/27/20`   <dbl> 2716, 8603, 1171, 1173, 2888, 694, 1153, 6240, 1765, 101…
## $ `11/28/20`   <dbl> 2735, 8733, 1173, 1179, 2922, 696, 1165, 6301, 1768, 102…
## $ `11/29/20`   <dbl> 2751, 8820, 1175, 1188, 2946, 700, 1173, 6366, 1772, 102…
## $ `11/30/20`   <dbl> 2780, 8890, 1178, 1196, 2997, 702, 1178, 6430, 1779, 103…
## $ `12/1/20`    <dbl> 2818, 9051, 1189, 1204, 3061, 701, 1186, 6598, 1827, 105…
## $ `12/2/20`    <dbl> 2873, 9163, 1206, 1239, 3100, 709, 1188, 6695, 1859, 105…
## $ `12/3/20`    <dbl> 2893, 9341, 1214, 1252, 3158, 709, 1200, 6809, 1875, 106…
## $ `12/4/20`    <dbl> 2945, 9501, 1217, 1270, 3231, 711, 1211, 6939, 1891, 108…
## $ `12/5/20`    <dbl> 2979, 9626, 1219, 1283, 3281, 713, 1225, 7027, 1901, 109…
## $ `12/6/20`    <dbl> 3005, 9728, 1223, 1293, 3299, 713, 1236, 7096, 1906, 109…
## $ `12/7/20`    <dbl> 3043, 9821, 1224, 1299, 3324, 714, 1244, 7165, 1915, 111…
## $ `12/8/20`    <dbl> 3087, 9974, 1240, 1317, 3426, 719, 1257, 7300, 1945, 112…
## $ `12/9/20`    <dbl> 3117, 10087, 1245, 1322, 3496, 722, 1263, 7392, 1961, 11…
## $ `12/10/20`   <dbl> 3186, 10288, 1258, 1359, 3600, 722, 1287, 7534, 1977, 11…
## $ `12/11/20`   <dbl> 3233, 10489, 1264, 1398, 3663, 723, 1289, 7658, 1982, 11…
## $ `12/12/20`   <dbl> 3258, 10665, 1269, 1417, 3744, 725, 1306, 7760, 1997, 11…
## $ `12/13/20`   <dbl> 3300, 10806, 1272, 1441, 3776, 728, 1330, 7813, 2013, 11…
## $ `12/14/20`   <dbl> 3329, 10898, 1275, 1455, 3803, 728, 1340, 7872, 2022, 12…
## $ `12/15/20`   <dbl> 3426, 11061, 1292, 1504, 3881, 733, 1332, 7966, 2040, 12…
## $ `12/16/20`   <dbl> 3510, 11212, 1296, 1520, 3950, 737, 1343, 8072, 2064, 12…
## $ `12/17/20`   <dbl> 3570, 11364, 1309, 1548, 4036, 742, 1368, 8290, 2076, 12…
## $ `12/18/20`   <dbl> 3647, 11556, 1318, 1577, 4118, 747, 1384, 8459, 2090, 12…
## $ `12/19/20`   <dbl> 3698, 11722, 1330, 1601, 4191, 752, 1393, 8594, 2116, 12…
## $ `12/20/20`   <dbl> 3741, 11827, 1336, 1613, 4218, 753, 1399, 8648, 2125, 12…
## $ `12/21/20`   <dbl> 3780, 11952, 1336, 1628, 4234, 754, 1405, 8684, 2133, 12…
## $ `12/22/20`   <dbl> 3841, 12155, 1363, 1660, 4313, 760, 1412, 8856, 2161, 13…
## $ `12/23/20`   <dbl> 3889, 12321, 1383, 1683, 4367, 765, 1423, 8968, 2176, 13…
## $ `12/24/20`   <dbl> 3942, 12521, 1390, 1711, 4405, 770, 1434, 9071, 2191, 13…
## $ `12/25/20`   <dbl> 3990, 12666, 1396, 1725, 4441, 777, 1448, 9167, 2200, 13…
## $ `12/26/20`   <dbl> 3999, 12708, 1398, 1739, 4446, 825, 1446, 9198, 2203, 13…
## $ `12/27/20`   <dbl> 4029, 12825, 1406, 1746, 4465, 827, 1452, 9232, 2214, 13…
## $ `12/28/20`   <dbl> 4065, 12962, 1417, 1762, 4483, 830, 1457, 9286, 2229, 13…
## $ `12/29/20`   <dbl> 4105, 13172, 1462, 1792, 4535, 834, 1482, 9345, 2275, 13…
## $ `12/30/20`   <dbl> 4164, 13392, 1492, 1817, 4584, 846, 1493, 9428, 2310, 14…
## $ `12/31/20`   <dbl> 4190, 13601, 1514, 1834, 4641, 859, 1508, 9494, 2341, 14…
## $ `1/1/21`     <dbl> 4239, 13823, 1517, 1854, 4693, 888, 1522, 9584, 2366, 14…
## $ `1/2/21`     <dbl> 4268, 13955, 1528, 1863, 4729, 892, 1530, 9692, 2386, 14…
## $ `1/3/21`     <dbl> 4305, 14064, 1530, 1882, 4746, 900, 1546, 9731, 2402, 14…
## $ `1/4/21`     <dbl> 4336, 14187, 1533, 1885, 4771, 910, 1554, 9752, 2415, 14…
## $ `1/5/21`     <dbl> 4546, 14440, 1575, 1923, 4849, 920, 1574, 9975, 2474, 14…
## $ `1/6/21`     <dbl> 4645, 14656, 1597, 1944, 4898, 925, 1583, 10109, 2519, 1…
## $ `1/7/21`     <dbl> 4705, 14845, 1614, 1981, 4957, 927, 1598, 10283, 2552, 1…
## $ `1/8/21`     <dbl> 4770, 15052, 1634, 2015, 5018, 949, 1610, 10372, 2592, 1…
## $ `1/9/21`     <dbl> 4847, 15202, 1648, 2038, 5047, 950, 1625, 10453, 2620, 1…
## $ `1/10/21`    <dbl> 4879, 15327, 1658, 2051, 5066, 953, 1632, 10497, 2639, 1…
## $ `1/11/21`    <dbl> 4902, 15417, 1663, 2060, 5080, 957, 1637, 10537, 2651, 1…
## $ `1/12/21`    <dbl> 4970, 15572, 1679, 2090, 5134, 967, 1649, 10668, 2697, 1…
## $ `1/13/21`    <dbl> 4998, 15701, 1685, 2109, 5170, 966, 1651, 10745, 2734, 1…
## $ `1/14/21`    <dbl> 5075, 15841, 1696, 2113, 5219, 971, 1669, 10863, 2757, 1…
## $ `1/15/21`    <dbl> 5103, 16002, 1712, 2130, 5264, 981, 1679, 10982, 2778, 1…
## $ `1/16/21`    <dbl> 5154, 16176, 1723, 2144, 5292, 987, 1684, 11078, 2818, 1…
## $ `1/17/21`    <dbl> 5184, 16251, 1729, 2151, 5304, 990, 1696, 11122, 2827, 1…
## $ `1/18/21`    <dbl> 5198, 16346, 1730, 2162, 5308, 991, 1702, 11161, 2842, 1…
## $ `1/19/21`    <dbl> 5227, 16513, 1738, 2170, 5320, 997, 1707, 11206, 2886, 1…
## $ `1/20/21`    <dbl> 5257, 16653, 1760, 2188, 5376, 1011, 1708, 11292, 2931, …
## $ `1/21/21`    <dbl> 5270, 16798, 1778, 2198, 5411, 1014, 1713, 11365, 2973, …
## $ `1/22/21`    <dbl> 5327, 16981, 1793, 2212, 5439, 1022, 1724, 11441, 3011, …
## $ `1/23/21`    <dbl> 5358, 17128, 1805, 2223, 5462, 1033, 1731, 11496, 3034, …
## $ `1/24/21`    <dbl> 5376, 17256, 1827, 2223, 5473, 1035, 1744, 11521, 3042, …
## $ `1/25/21`    <dbl> 5407, 17333, 1834, 2229, 5485, 1046, 1748, 11555, 3054, …
## $ `1/26/21`    <dbl> 5440, 17496, 1882, 2247, 5517, 1058, 1759, 11626, 3085, …
## $ `1/27/21`    <dbl> 5499, 17629, 1898, 2261, 5568, 1074, 1766, 11730, 3137, …
## $ `1/28/21`    <dbl> 5554, 17779, 1920, 2271, 5612, 1079, 1788, 11833, 3159, …
## $ `1/29/21`    <dbl> 5596, 17922, 1931, 2284, 5655, 1075, 1800, 11918, 3174, …
## $ `1/30/21`    <dbl> 5596, 17922, 1931, 2284, 5655, 1075, 1800, 11918, 3174, …
## $ `1/31/21`    <dbl> 5669, 18126, 1951, 2307, 5713, 1086, 1812, 12011, 3203, …
## $ `2/1/21`     <dbl> 5683, 18211, 1956, 2309, 5720, 1089, 1827, 12062, 3210, …
## $ `2/2/21`     <dbl> 5723, 18344, 1966, 2319, 5745, 1087, 1833, 12102, 3219, …
## $ `2/3/21`     <dbl> 5753, 18418, 1981, 2321, 5768, 1093, 1838, 12179, 3233, …
## $ `2/4/21`     <dbl> 5811, 18494, 1989, 2327, 5842, 1107, 1847, 12253, 3239, …
## $ `2/5/21`     <dbl> 5824, 18568, 1994, 2331, 5871, 1113, 1853, 12325, 3249, …
## $ `2/6/21`     <dbl> 5856, 18668, 2002, 2334, 5908, 1121, 1863, 12368, 3259, …
## $ `2/7/21`     <dbl> 5869, 18723, 2008, 2339, 5915, 1128, 1865, 12402, 3263, …
## $ `2/8/21`     <dbl> 5881, 18763, 2008, 2346, 5920, 1132, 1868, 12426, 3266, …
## $ `2/9/21`     <dbl> 5910, 18824, 2019, 2362, 5929, 1132, 1872, 12477, 3283, …
## $ `2/10/21`    <dbl> 5930, 18888, 2024, 2368, 5937, 1131, 1882, 12498, 3291, …
## $ `2/11/21`    <dbl> 5970, 18960, 2030, 2377, 5955, 1136, 1886, 12539, 3305, …
## $ `2/12/21`    <dbl> 5984, 18994, 2036, 2385, 5953, 1137, 1892, 12577, 3313, …
## $ `2/13/21`    <dbl> 6002, 19051, 2040, 2393, 5957, 1139, 1898, 12629, 3318, …
## $ `2/14/21`    <dbl> 6023, 19105, 2042, 2395, 5961, 1142, 1902, 12700, 3321, …
## $ `2/15/21`    <dbl> 6024, 19136, 2044, 2397, 5973, 1142, 1905, 12725, 3325, …
## $ `2/16/21`    <dbl> 6038, 19176, 2055, 2400, 5987, 1145, 1910, 12756, 3336, …
## $ `2/17/21`    <dbl> 6050, 19267, 2053, 2399, 5997, 1143, 1924, 12784, 3338, …
## $ `2/18/21`    <dbl> 6071, 19324, 2057, 2405, 6008, 1144, 1930, 12833, 3348, …
## $ `2/19/21`    <dbl> 6079, 19361, 2061, 2411, 6021, 1147, 1934, 12860, 3358, …
## $ `2/20/21`    <dbl> 6092, 19392, 2067, 2414, 6040, 1149, 1938, 12915, 3364, …
## $ `2/21/21`    <dbl> 6117, 19433, 2070, 2416, 6042, 1151, 1940, 12940, 3367, …
## $ `2/22/21`    <dbl> 6121, 19461, 2074, 2417, 6043, 1153, 1945, 13017, 3367, …
## $ `2/23/21`    <dbl> 6143, 19554, 2084, 2432, 6058, 1160, 1948, 13063, 3382, …
## $ st_county    <chr> "01001", "01003", "01005", "01007", "01009", "01011", "0…

Reshape the infection data file into a tall format that allows for more efficient generation of time-series analysis of data. We are using the pivot_longer function instead of the now depricated gather function frequently referenced in the context of reshaping data.

c19_working_tall <- c19_working_wide %>% 
  pivot_longer(
    -c(UID, st_county, Combined_Key),
    names_to = "caldate",
    values_to = "count"
  )
glimpse(c19_working_tall)
## Observations: 1,332,660
## Variables: 5
## $ UID          <dbl> 84001001, 84001001, 84001001, 84001001, 84001001, 840010…
## $ Combined_Key <chr> "Autauga, Alabama, US", "Autauga, Alabama, US", "Autauga…
## $ st_county    <chr> "01001", "01001", "01001", "01001", "01001", "01001", "0…
## $ caldate      <chr> "1/22/20", "1/23/20", "1/24/20", "1/25/20", "1/26/20", "…
## $ count        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…

Build a reference table of population density values from a combination of the ACS and LAD data

pop_density <- lad_working %>% 
  inner_join(acs5_working, by = "st_county") %>% 
  mutate(
    den_total = pop_total/land_area_sqmi,
    den_lt20 = pop_lt20/land_area_sqmi,
    den_gte65 = pop_gte65/land_area_sqmi
  ) %>% 
  select(
    -starts_with("pop"),
    -starts_with("pct"),
    -starts_with("area"), 
    -id,
    pop_total,
    pop_lt20,
    pop_gte65
  )
glimpse(pop_density)
## Observations: 3,140
## Variables: 8
## $ st_county      <chr> "01001", "01003", "01005", "01007", "01009", "01011", …
## $ land_area_sqmi <dbl> 594.4, 1589.8, 884.9, 622.6, 644.8, 622.8, 776.8, 605.…
## $ den_total      <dbl> 92.86, 130.90, 29.14, 36.18, 89.40, 16.62, 25.78, 189.…
## $ den_lt20       <dbl> 24.724, 31.657, 6.725, 8.442, 22.999, 4.082, 6.519, 46…
## $ den_gte65      <dbl> 13.542, 25.579, 5.237, 5.880, 15.871, 2.595, 4.899, 31…
## $ pop_total      <dbl> 55200, 208107, 25782, 22527, 57645, 10352, 20025, 1150…
## $ pop_lt20       <dbl> 14697, 50328, 5951, 5256, 14829, 2542, 5064, 28337, 77…
## $ pop_gte65      <dbl> 8050, 40665, 4634, 3661, 10233, 1616, 3806, 19386, 640…

Perform an analysis (describe & summarize) and visualize results

Aggregate data across all geographies by calendar date

master_ts <- c19_working_tall %>% 
  mutate(
    caldate = mdy(caldate),
    day_num = as.numeric(caldate - min(caldate))
  ) %>% 
  group_by(caldate, day_num) %>% 
  summarize(
    ct = sum(count)
  ) %>% 
  mutate(
    ln_ct = log1p(ct)
  ) %>% 
  filter(ct > 0)

glimpse(master_ts)
## Observations: 399
## Variables: 4
## Groups: caldate [399]
## $ caldate <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26, …
## $ day_num <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,…
## $ ct      <dbl> 1, 1, 2, 2, 5, 5, 5, 6, 6, 8, 8, 8, 11, 11, 11, 12, 12, 12, 1…
## $ ln_ct   <dbl> 0.6931, 0.6931, 1.0986, 1.0986, 1.7918, 1.7918, 1.7918, 1.945…
master_plot <- ggplot(master_ts, mapping = aes(x = day_num, y = ct)) + 
  geom_line() +
  xlab("Number of Days Since First Confirmed Infection") +
  ylab("Confirmed Infection Count")

master_plot + ggtitle("US - Number of confirmed cases")

master_plot + ggtitle("US - Number of confirmed cases") + geom_smooth(method="lm")

master_plot + ggtitle("US - Number of confirmed cases (log scale)") + scale_y_continuous(trans='log2')

ln_plot <- ggplot(master_ts, mapping = aes(x = day_num, y = ln_ct)) + 
  geom_line() +
  xlab("Number of Days") +
  ylab("Confirmed Infection Count (ln)")

ln_plot + ggtitle("US - Number of confirmed cases (ln)")

ln_plot + ggtitle("US - Number of confirmed cases (ln)") + geom_smooth(method="lm")

Plot growth by state

# calculate the start date for confirmed infections within the state (based on first confirmed value within a county within the state)
state_start <- c19_working_tall %>% 
  filter(str_length(st_county) == 5 & count > 0) %>% 
  mutate(
    st_fips = str_sub(st_county, 1, 2),
    caldate = mdy(caldate)
  ) %>% 
  inner_join(state_fips) %>% 
  group_by(Areaname) %>% 
  summarize(
    start_date = min(caldate)
  )
glimpse(state_start)
## Observations: 51
## Variables: 2
## $ Areaname   <chr> "ALABAMA", "ALASKA", "ARIZONA", "ARKANSAS", "CALIFORNIA", …
## $ start_date <date> 2020-03-11, 2020-03-13, 2020-01-26, 2020-03-14, 2020-01-2…
# build the datafram from which plots will be generated
state_ts <- c19_working_tall %>% 
  filter(str_length(st_county) == 5 & count > 0) %>% 
  mutate(
    st_fips = str_sub(st_county, 1, 2),
    caldate = mdy(caldate)
  ) %>% 
  inner_join(state_fips) %>%
  inner_join(pop_density) %>% 
  group_by(Areaname, caldate) %>% 
  summarize(
    ct = sum(count),
    pop_total = sum(pop_total),
    land_area_sqmi = sum(land_area_sqmi),
    den_total = pop_total/land_area_sqmi,
    rate_infection = (ct/pop_total) * 100000
  )  %>% 
 left_join(state_start, by = "Areaname") %>% 
 mutate(
    ln_ct = log1p(ct),
    day_num = as.numeric(caldate - start_date)
  )

glimpse(state_ts)
## Observations: 18,213
## Variables: 10
## Groups: Areaname [51]
## $ Areaname       <chr> "ALABAMA", "ALABAMA", "ALABAMA", "ALABAMA", "ALABAMA",…
## $ caldate        <date> 2020-03-11, 2020-03-12, 2020-03-13, 2020-03-14, 2020-…
## $ ct             <dbl> 3, 4, 8, 15, 28, 36, 51, 61, 88, 115, 149, 180, 224, 2…
## $ pop_total      <dbl> 526206, 883766, 1624870, 1624870, 2203525, 2203525, 24…
## $ land_area_sqmi <dbl> 2666, 3468, 5197, 5197, 8180, 8180, 9417, 10154, 13506…
## $ den_total      <dbl> 197.38, 254.87, 312.64, 312.64, 269.39, 269.39, 255.48…
## $ rate_infection <dbl> 0.5701, 0.4526, 0.4923, 0.9232, 1.2707, 1.6337, 2.1198…
## $ start_date     <date> 2020-03-11, 2020-03-11, 2020-03-11, 2020-03-11, 2020-…
## $ ln_ct          <dbl> 1.386, 1.609, 2.197, 2.773, 3.367, 3.611, 3.951, 4.127…
## $ day_num        <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, …
state_plot <- ggplot(state_ts, mapping = aes(x = day_num, y = ct, color = Areaname)) + 
  #geom_hex()+
  geom_point(size = .3) + 
  xlab("Number of Days Since First Confirmed Infection") +
  ylab("Confirmed Infection Count") +
  theme(legend.position="none") +
  theme(legend.text=element_text(size=6))

state_plot + ggtitle("Number of confirmed cases - by state")

state_plot + ggtitle("Number of confirmed cases - by state") + theme_tufte() + theme(legend.position="none")

# note use of geom_jitter() instead of geom_point
ln_plot <- ggplot(state_ts, mapping = aes(x = day_num, y = ln_ct, color = Areaname)) + 
  #geom_hex()+
  geom_jitter(size = .3) +
  xlab("Number of Days Since First Confirmed Infection (ln)") +
  ylab("Confirmed Infection Count (ln)") +
  ggtitle("Number of confirmed cases by date") +
  theme(legend.position="none") +
  theme(legend.text=element_text(size=6))

ln_plot + ggtitle("Number of confirmed cases (ln) - by state")

ln_plot + ggtitle("Number of confirmed cases (ln) - by state") + theme_tufte() + theme(legend.position="none")

What are the states that are showing long growth curves (is there a problem?)

# what are the states that have the long (and early slow) growth curves?
my_colors <- brewer.pal(8, "Dark2")
state_density_data_10d <- state_ts %>% 
  filter(day_num == 10) %>% 
  group_by(Areaname, start_date) %>% 
  summarize(
    max_days = max(day_num),
    max_infection = max(ct),
    max_rate_infection = max(rate_infection),
    avg_density = mean(den_total)
  ) %>% 
  arrange(desc(max_rate_infection))

state_density_data_20d <- state_ts %>% 
  filter(day_num == 20) %>% 
  group_by(Areaname, start_date) %>% 
  summarize(
    max_days = max(day_num),
    max_infection = max(ct),
    max_rate_infection = max(rate_infection),
    avg_density = mean(den_total)
  ) %>% 
  arrange(desc(max_rate_infection))

state_density_data_30d <- state_ts %>% 
  filter(day_num == 30) %>% 
  group_by(Areaname, start_date) %>% 
  summarize(
    max_days = max(day_num),
    max_infection = max(ct),
    max_rate_infection = max(rate_infection),
    avg_density = mean(den_total)
  ) %>% 
  arrange(desc(max_rate_infection))

state_density_data_40d <- state_ts %>% 
  filter(day_num == 40) %>% 
  group_by(Areaname, start_date) %>% 
  summarize(
    max_days = max(day_num),
    max_infection = max(ct),
    max_rate_infection = max(rate_infection),
    avg_density = mean(den_total)
  ) %>% 
  arrange(desc(max_rate_infection))

state_density_data_50d <- state_ts %>% 
  filter(day_num == 50) %>% 
  group_by(Areaname, start_date) %>% 
  summarize(
    max_days = max(day_num),
    max_infection = max(ct),
    max_rate_infection = max(rate_infection),
    avg_density = mean(den_total)
  ) %>% 
  arrange(desc(max_rate_infection))

state_density_data_60d <- state_ts %>% 
  filter(day_num == 60) %>% 
  group_by(Areaname, start_date) %>% 
  summarize(
    max_days = max(day_num),
    max_infection = max(ct),
    max_rate_infection = max(rate_infection),
    avg_density = mean(den_total)
  ) %>% 
  arrange(desc(max_rate_infection))

state_density_data_70d <- state_ts %>% 
  filter(day_num == 70) %>% 
  group_by(Areaname, start_date) %>% 
  summarize(
    max_days = max(day_num),
    max_infection = max(ct),
    max_rate_infection = max(rate_infection),
    avg_density = mean(den_total)
  ) %>% 
  arrange(desc(max_rate_infection))

state_density_data_80d <- state_ts %>% 
  filter(day_num == 80) %>% 
  group_by(Areaname, start_date) %>% 
  summarize(
    max_days = max(day_num),
    max_infection = max(ct),
    max_rate_infection = max(rate_infection),
    avg_density = mean(den_total)
  ) %>% 
  arrange(desc(max_rate_infection))

base_plot <- ggplot() +
  geom_jitter(data = state_density_data_10d, mapping = aes(x = max_rate_infection, y = avg_density), color = my_colors[1], shape = 1) +
  geom_jitter(data = state_density_data_20d, mapping = aes(x = max_rate_infection, y = avg_density), color = my_colors[2], shape = 2) +
  geom_jitter(data = state_density_data_30d, mapping = aes(x = max_rate_infection, y = avg_density), color = my_colors[3], shape = 3) +
  geom_jitter(data = state_density_data_40d, mapping = aes(x = max_rate_infection, y = avg_density), color = my_colors[4], shape = 4) +
  geom_jitter(data = state_density_data_50d, mapping = aes(x = max_rate_infection, y = avg_density), color = my_colors[4], shape = 5) +
  geom_jitter(data = state_density_data_60d, mapping = aes(x = max_rate_infection, y = avg_density), color = my_colors[4], shape = 6) +
  geom_jitter(data = state_density_data_70d, mapping = aes(x = max_rate_infection, y = avg_density), color = my_colors[4], shape = 7) +
  geom_jitter(data = state_density_data_80d, mapping = aes(x = max_rate_infection, y = avg_density), color = my_colors[4], shape = 8) +
  theme_tufte() +
  xlab("Infection rate (cases/100,000) ") +
  ylab("Average population density (persons/mi^2)") +
  ggtitle("Infection Rate vs. Population Density Day 10, 20, 30, 40, 50, 60, 70")
base_plot

base_plot + 
  scale_y_continuous(trans='log2')+
  scale_x_continuous(trans="log2")

print(state_density_data_10d)
## # A tibble: 51 x 6
## # Groups:   Areaname [51]
##    Areaname       start_date max_days max_infection max_rate_infect… avg_density
##    <chr>          <date>        <dbl>         <dbl>            <dbl>       <dbl>
##  1 DISTRICT OF C… 2020-03-16       10           231            33.7     11212.  
##  2 LOUISIANA      2020-03-11       10           754            19.0       173.  
##  3 ARKANSAS       2020-03-14       10           206             9.10       87.2 
##  4 MISSISSIPPI    2020-03-12       10           207             8.63       78.8 
##  5 WEST VIRGINIA  2020-03-18       10            96             8.23      135.  
##  6 MAINE          2020-03-12       10            81             8.09      107.  
##  7 MICHIGAN       2020-03-11       10           625             7.72      450.  
##  8 NORTH DAKOTA   2020-03-12       10            28             7.31       40.2 
##  9 WYOMING        2020-03-12       10            26             7.15        8.39
## 10 IDAHO          2020-03-14       10            81             6.98       61.4 
## # … with 41 more rows
state_density_data_10d %>% 
  kable() %>% 
  kable_styling()
Areaname start_date max_days max_infection max_rate_infection avg_density
DISTRICT OF COLUMBIA 2020-03-16 10 231 33.7474 11212.088
LOUISIANA 2020-03-11 10 754 19.0192 172.866
ARKANSAS 2020-03-14 10 206 9.1030 87.182
MISSISSIPPI 2020-03-12 10 207 8.6310 78.777
WEST VIRGINIA 2020-03-18 10 96 8.2317 134.540
MAINE 2020-03-12 10 81 8.0883 107.163
MICHIGAN 2020-03-11 10 625 7.7183 449.891
NORTH DAKOTA 2020-03-12 10 28 7.3053 40.166
WYOMING 2020-03-12 10 26 7.1477 8.387
IDAHO 2020-03-14 10 81 6.9832 61.383
ALASKA 2020-03-13 10 39 6.4268 10.019
MONTANA 2020-03-14 10 45 6.1862 24.812
CONNECTICUT 2020-03-10 10 194 5.4167 739.620
WISCONSIN 2020-03-10 10 219 4.7780 231.706
DELAWARE 2020-03-11 10 45 4.7394 487.283
NEW MEXICO 2020-03-11 10 55 4.6286 39.364
ALABAMA 2020-03-11 10 149 4.5330 185.927
SOUTH DAKOTA 2020-03-11 10 14 3.9310 47.401
IOWA 2020-03-09 10 44 3.7620 144.903
VERMONT 2020-03-08 10 13 3.4414 87.412
NEBRASKA 2020-03-06 10 18 3.0552 295.425
COLORADO 2020-03-05 10 136 2.7781 157.500
NEW YORK 2020-03-03 10 379 2.4986 1432.442
UTAH 2020-03-07 10 35 2.1907 144.850
KANSAS 2020-03-08 10 22 2.0451 231.659
OHIO 2020-03-10 10 173 2.0375 588.599
KENTUCKY 2020-03-06 10 21 1.7022 579.894
VIRGINIA 2020-03-08 10 79 1.6710 1029.982
MINNESOTA 2020-03-06 10 53 1.5532 494.382
SOUTH CAROLINA 2020-03-07 10 47 1.5463 322.846
NEW HAMPSHIRE 2020-03-02 10 6 1.5192 164.321
PENNSYLVANIA 2020-03-06 10 79 1.1220 905.944
INDIANA 2020-03-06 10 25 1.0234 463.620
OREGON 2020-02-29 10 14 1.0122 73.251
TENNESSEE 2020-03-05 10 32 0.9968 684.851
GEORGIA 2020-03-03 10 42 0.9514 679.700
NEW JERSEY 2020-03-05 10 72 0.9274 1712.438
OKLAHOMA 2020-03-07 10 17 0.8473 352.663
MARYLAND 2020-03-06 10 42 0.8181 1118.423
HAWAII 2020-03-07 10 10 0.8168 513.931
RHODE ISLAND 2020-03-01 10 5 0.7880 1549.531
NEVADA 2020-03-05 10 20 0.7716 182.619
NORTH CAROLINA 2020-03-02 10 17 0.5176 598.802
TEXAS 2020-03-05 10 64 0.3592 1118.202
MISSOURI 2020-03-03 10 5 0.3128 1283.834
FLORIDA 2020-03-02 10 30 0.2718 744.295
MASSACHUSETTS 2020-01-29 10 2 0.1239 1028.763
WASHINGTON 2020-01-22 10 1 0.0462 1022.541
CALIFORNIA 2020-01-26 10 6 0.0394 2025.153
ILLINOIS 2020-01-24 10 2 0.0383 5525.815
ARIZONA 2020-01-26 10 1 0.0235 462.375
# Another way to do this - by combining out three dataframes into 1 with a new column that identifies the day number for the values

plot_data <-  bind_rows(
  mutate(state_density_data_10d, day_num = "d10"),
  mutate(state_density_data_20d, day_num = "d20"), 
  mutate(state_density_data_30d, day_num = "d30"), 
  mutate(state_density_data_40d, day_num = "d40"),
  mutate(state_density_data_50d, day_num = "d50"),
  mutate(state_density_data_60d, day_num = "d60"), 
  mutate(state_density_data_70d, day_num = "d70"), 
  mutate(state_density_data_80d, day_num = "d80")
  )
base_plot <- ggplot(plot_data, mapping = aes(x = max_rate_infection, y = avg_density, color = day_num, shape = day_num)) +
  geom_jitter() +
  scale_color_brewer(palette = "Dark2") +
  theme_tufte() +
  xlab("Infection rate (cases/100,000) ") +
  ylab("Average population density (persons/mi^2)") +
  ggtitle("Infection Rate vs. Population Density Day 10, 20, 30, 40, 50, 60, 70")

base_plot
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 8. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 102 rows containing missing values (geom_point).

base_plot + scale_shape_manual(values = c(1, 2, 3, 4, 5, 6, 7, 8))

base_plot + 
  scale_shape_manual(values = c(1, 2, 3, 4, 5, 6, 7, 8)) +
  scale_y_continuous(trans='log2') +
  scale_x_continuous(trans="log2")

print(plot_data)
## # A tibble: 408 x 7
## # Groups:   Areaname [51]
##    Areaname start_date max_days max_infection max_rate_infect… avg_density
##    <chr>    <date>        <dbl>         <dbl>            <dbl>       <dbl>
##  1 DISTRIC… 2020-03-16       10           231            33.7     11212.  
##  2 LOUISIA… 2020-03-11       10           754            19.0       173.  
##  3 ARKANSAS 2020-03-14       10           206             9.10       87.2 
##  4 MISSISS… 2020-03-12       10           207             8.63       78.8 
##  5 WEST VI… 2020-03-18       10            96             8.23      135.  
##  6 MAINE    2020-03-12       10            81             8.09      107.  
##  7 MICHIGAN 2020-03-11       10           625             7.72      450.  
##  8 NORTH D… 2020-03-12       10            28             7.31       40.2 
##  9 WYOMING  2020-03-12       10            26             7.15        8.39
## 10 IDAHO    2020-03-14       10            81             6.98       61.4 
## # … with 398 more rows, and 1 more variable: day_num <chr>
plot_data %>% 
  kable() %>% 
  kable_styling()
Areaname start_date max_days max_infection max_rate_infection avg_density day_num
DISTRICT OF COLUMBIA 2020-03-16 10 231 33.7474 11212.088 d10
LOUISIANA 2020-03-11 10 754 19.0192 172.866 d10
ARKANSAS 2020-03-14 10 206 9.1030 87.182 d10
MISSISSIPPI 2020-03-12 10 207 8.6310 78.777 d10
WEST VIRGINIA 2020-03-18 10 96 8.2317 134.540 d10
MAINE 2020-03-12 10 81 8.0883 107.163 d10
MICHIGAN 2020-03-11 10 625 7.7183 449.891 d10
NORTH DAKOTA 2020-03-12 10 28 7.3053 40.166 d10
WYOMING 2020-03-12 10 26 7.1477 8.387 d10
IDAHO 2020-03-14 10 81 6.9832 61.383 d10
ALASKA 2020-03-13 10 39 6.4268 10.019 d10
MONTANA 2020-03-14 10 45 6.1862 24.812 d10
CONNECTICUT 2020-03-10 10 194 5.4167 739.620 d10
WISCONSIN 2020-03-10 10 219 4.7780 231.706 d10
DELAWARE 2020-03-11 10 45 4.7394 487.283 d10
NEW MEXICO 2020-03-11 10 55 4.6286 39.364 d10
ALABAMA 2020-03-11 10 149 4.5330 185.927 d10
SOUTH DAKOTA 2020-03-11 10 14 3.9310 47.401 d10
IOWA 2020-03-09 10 44 3.7620 144.903 d10
VERMONT 2020-03-08 10 13 3.4414 87.412 d10
NEBRASKA 2020-03-06 10 18 3.0552 295.425 d10
COLORADO 2020-03-05 10 136 2.7781 157.500 d10
NEW YORK 2020-03-03 10 379 2.4986 1432.442 d10
UTAH 2020-03-07 10 35 2.1907 144.850 d10
KANSAS 2020-03-08 10 22 2.0451 231.659 d10
OHIO 2020-03-10 10 173 2.0375 588.599 d10
KENTUCKY 2020-03-06 10 21 1.7022 579.894 d10
VIRGINIA 2020-03-08 10 79 1.6710 1029.982 d10
MINNESOTA 2020-03-06 10 53 1.5532 494.382 d10
SOUTH CAROLINA 2020-03-07 10 47 1.5463 322.846 d10
NEW HAMPSHIRE 2020-03-02 10 6 1.5192 164.321 d10
PENNSYLVANIA 2020-03-06 10 79 1.1220 905.944 d10
INDIANA 2020-03-06 10 25 1.0234 463.620 d10
OREGON 2020-02-29 10 14 1.0122 73.251 d10
TENNESSEE 2020-03-05 10 32 0.9968 684.851 d10
GEORGIA 2020-03-03 10 42 0.9514 679.700 d10
NEW JERSEY 2020-03-05 10 72 0.9274 1712.438 d10
OKLAHOMA 2020-03-07 10 17 0.8473 352.663 d10
MARYLAND 2020-03-06 10 42 0.8181 1118.423 d10
HAWAII 2020-03-07 10 10 0.8168 513.931 d10
RHODE ISLAND 2020-03-01 10 5 0.7880 1549.531 d10
NEVADA 2020-03-05 10 20 0.7716 182.619 d10
NORTH CAROLINA 2020-03-02 10 17 0.5176 598.802 d10
TEXAS 2020-03-05 10 64 0.3592 1118.202 d10
MISSOURI 2020-03-03 10 5 0.3128 1283.834 d10
FLORIDA 2020-03-02 10 30 0.2718 744.295 d10
MASSACHUSETTS 2020-01-29 10 2 0.1239 1028.763 d10
WASHINGTON 2020-01-22 10 1 0.0462 1022.541 d10
CALIFORNIA 2020-01-26 10 6 0.0394 2025.153 d10
ILLINOIS 2020-01-24 10 2 0.0383 5525.815 d10
ARIZONA 2020-01-26 10 1 0.0235 462.375 d10
DISTRICT OF COLUMBIA 2020-03-16 20 1002 146.3846 11212.088 d20
LOUISIANA 2020-03-11 20 5215 112.6130 114.549 d20
NEW YORK 2020-03-03 20 20777 109.1947 512.861 d20
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IDAHO 2020-03-14 20 891 56.0670 26.914 d20
NEW JERSEY 2020-03-05 20 3472 39.0910 1207.721 d20
MISSISSIPPI 2020-03-12 20 1073 36.7035 66.242 d20
DELAWARE 2020-03-11 20 319 33.5968 487.283 d20
VERMONT 2020-03-08 20 204 33.3440 72.221 d20
MONTANA 2020-03-14 20 243 27.9825 15.741 d20
WEST VIRGINIA 2020-03-18 20 412 26.0095 96.097 d20
WYOMING 2020-03-12 20 130 25.9998 6.594 d20
MAINE 2020-03-12 20 297 25.5721 72.312 d20
ALASKA 2020-03-13 20 150 24.4997 2.971 d20
ARKANSAS 2020-03-14 20 657 24.0688 68.126 d20
WISCONSIN 2020-03-10 20 1230 23.9688 154.805 d20
ALABAMA 2020-03-11 20 1063 23.0534 105.683 d20
NORTH DAKOTA 2020-03-12 20 142 23.0285 17.083 d20
COLORADO 2020-03-05 20 1069 20.0354 94.081 d20
UTAH 2020-03-07 20 438 18.6492 151.922 d20
OHIO 2020-03-10 20 1933 17.3785 339.450 d20
NEW MEXICO 2020-03-11 20 315 16.7454 27.497 d20
SOUTH DAKOTA 2020-03-11 20 108 15.2156 23.318 d20
PENNSYLVANIA 2020-03-06 20 1795 14.8609 366.841 d20
IOWA 2020-03-09 20 336 13.3736 82.343 d20
TENNESSEE 2020-03-05 20 735 12.8598 235.944 d20
KANSAS 2020-03-08 20 266 11.8573 99.332 d20
SOUTH CAROLINA 2020-03-07 20 542 11.3921 178.289 d20
INDIANA 2020-03-06 20 645 11.0780 245.319 d20
NEVADA 2020-03-05 20 308 10.8318 54.264 d20
VIRGINIA 2020-03-08 20 740 10.2787 322.858 d20
MARYLAND 2020-03-06 20 583 9.8827 674.788 d20
OKLAHOMA 2020-03-07 20 322 9.7079 106.538 d20
GEORGIA 2020-03-03 20 727 8.6310 357.972 d20
MINNESOTA 2020-03-06 20 344 7.6029 161.825 d20
HAWAII 2020-03-07 20 100 7.0326 221.812 d20
MISSOURI 2020-03-03 20 254 6.1712 271.594 d20
NEW HAMPSHIRE 2020-03-02 20 73 5.8356 185.113 d20
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RHODE ISLAND 2020-03-01 20 54 5.1107 1022.045 d20
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TEXAS 2020-03-05 20 1248 4.9831 281.553 d20
FLORIDA 2020-03-02 20 1003 4.9803 508.701 d20
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OREGON 2020-02-29 20 114 3.1949 89.781 d20
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DELAWARE 2020-03-11 30 1317 138.7053 487.283 d30
VERMONT 2020-03-08 30 572 92.4416 72.345 d30
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MISSISSIPPI 2020-03-12 30 2642 88.4371 64.232 d30
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UTAH 2020-03-07 30 1596 60.3389 48.767 d30
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DELAWARE 2020-03-11 40 2714 285.8362 487.283 d40
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OREGON 2020-02-29 80 3726 91.5307 47.931 d80
ALASKA 2020-03-13 80 491 69.2391 1.395 d80
CALIFORNIA 2020-01-26 80 26699 68.3270 275.685 d80
ARIZONA 2020-01-26 80 3964 57.0632 61.154 d80
MONTANA 2020-03-14 80 523 56.2302 11.801 d80
HAWAII 2020-03-07 80 633 44.5162 221.812 d80
purl('workflow.rmd', output = "workflow.R")
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## [1] "workflow.R"